Directoryrank

Ordering

Pages

In

Web

Directories

Web

Directories

are

repositories

of

Web

pages

organized

in

a

hierarchy

of

topics

and

sub-topics

In

this

paper

we

present

DirectoryRank

a

ranking

framework

that

orders

the

pages

within

a

given

topic

according

to

how

informative

they

are

about

the

topic

Our

method

works

in

three

steps

first

it

processes

Web

pages

within

a

topic

in

order

to

extract

structures

that

are

called

lexical

chains

which

are

then

used

for

measuring

how

informative

a

page

is

for

a

particular

topic

Then

it

measures

the

relative

semantic

similarity

of

the

pages

within

a

topic

Finally

the

two

metrics

are

combined

for

ranking

all

the

pages

within

a

topic

before

presenting

them

to

the

users

H.

-LSB-

Information

Search

and

Retrieval

-RSB-

information

filtering

retrieval

models

H.

m

-LSB-

Information

Systems

-RSB-

Miscellaneous

Algorithms

Design

Experimentation

Measurement

A

Web

Directory

is

a

repository

of

Web

pages

that

are

organized

in

a

topic

hierarchy

Typically

Directory

users

locate

the

information

sought

simply

by

browsing

through

the

topic

hierarchy

identifying

the

relevant

topics

and

finally

examining

the

pages

listed

under

the

relevant

topics

Given

the

current

size

and

the

high

growth

rate

of

the

Web

-LSB-

-RSB-

a

comprehensive

Web

Directory

may

contain

thousands

of

pages

within

a

particular

category

In

such

a

case

it

might

be

impossible

for

a

user

to

look

through

all

the

relevant

pages

within

a

particular

topic

in

order

to

identify

the

ones

that

best

represent

the

current

topic

Practically

it

would

be

more

time-efficient

for

a

user

to

view

the

Web

pages

in

order

of

importance

for

a

particular

topic

rather

than

go

through

a

large

list

of

pages

One

way

to

alleviate

this

problem

is

to

use

a

ranking

function

which

will

order

the

pages

according

to

how

informative

they

are

of

the

topic

that

they

belong

to

Currently

the

Open

Directory

Project

-LSB-

-RSB-

lists

the

pages

within

a

category

alphabetically

while

the

Google

Directory

-LSB-

-RSB-

orders

the

pages

within

a

category

according

to

their

PageRank

-LSB-

-RSB-

value

on

the

Web

While

these

rankings

can

work

well

in

some

cases

they

do

not

directly

capture

the

closeness

of

the

pages

to

the

topic

that

they

belong

to

In

this

paper

we

present

DirectoryRank

a

new

ranking

framework

that

we

have

developed

in

order

to

alleviate

the

problem

of

ranking

the

pages

within

a

topic

based

on

how

informative

these

pages

are

to

the

topic

DirectoryRank

is

based

on

the

intuition

that

the

quality

-LRB-

or

informativeness

-RRB-

of

a

Web

page

with

respect

to

a

particular

topic

is

determined

by

the

amount

of

information

that

the

page

communicates

about

the

given

topic

relative

to

the

other

pages

that

are

categorized

in

the

same

topic

Our

method

takes

as

input

a

collection

of

Web

pages

that

we

would

like

to

rank

along

with

a

Web

Directory

's

topic

hierarchy

that

we

would

like

to

use

At

a

high

level

our

method

proceeds

as

follows

first

we

identify

the

most

important

words

inside

every

page

and

we

link

them

together

creating

lexical

chains

We

then

use

the

topic

hierarchy

and

the

pages

lexical

chains

to

compute

the

relatedness

-LRB-

or

importance

-RRB-

of

the

pages

to

each

of

their

corresponding

topics

Having

determined

the

pages

topic

importance

we

measure

the

relative

semantic

similarity

among

the

pages

that

relate

to

the

same

topic

The

semantic

similarity

indicates

the

amount

of

content

that

important

pages

in

some

topic

share

with

each

other

Finally

we

employ

our

DirectoryRank

algorithm

that

uses

the

topic

importance

scores

in

conjunction

with

the

semantic

similarities

of

the

pages

in

order

to

compute

the

ranking

order

of

the

pages

within

a

Directory

topic

In

order

to

study

the

effectiveness

of

DirectoryRank

in

identifying

the

most

informative

pages

within

a

particular

topic

we

applied

our

method

to

the

ranking

of

Web

pages

listed

in

topics

in

the

Google

Directory

We

have

compared

the

rankings

induced

by

DirectoryRank

to

the

rankings

induced

by

PageRank

for

the

pages

listed

in

those

topics

Our

comparison

reveals

that

the

two

rankings

have

different

merits

and

thus

they

are

useful

in

different

tasks

To

delve

into

the

two

rankings

effectiveness

and

investigate

which

is

more

useful

for

ordering

pages

in

Directories

topics

we

conducted

a

user

study

where

we

asked

a

group

of

individuals

to

compare

the

rankings

delivered

by

PageRank

to

the

rankings

delivered

by

DirectoryRank

and

indicate

which

of

the

two

is

deemed

as

more

useful

Our

results

show

that

in

most

cases

the

users

perceived

DirectoryRank

to

be

more

topic-informative

than

PageRank

The

rest

of

the

paper

is

organized

as

follows

We

start

our

discussion

in

Section

with

a

brief

introduction

to

PageRank

which

is

currently

employed

by

the

Google

Directory

in

order

to

rank

pages

In

Section

we

briefly

present

the

topic

hierarchy

that

we

use

in

our

study

as

well

as

the

process

we

follow

for

representing

Web

pages

into

lexical

chains

We

also

show

how

we

explore

the

topic

Permission

to

make

digital

or

hard

copies

of

all

or

part

of

this

work

for

personal

or

classroom

use

is

granted

without

fee

provided

that

copies

are

not

made

or

distributed

for

profit

or

commercial

advantage

and

that

copies

bear

this

notice

and

the

full

citation

on

the

first

page

To

copy

otherwise

or

republish

to

post

on

servers

or

to

redistribute

to

lists

requires

prior

specific

permission

and/or

a

fee

WIDM

November

Bremen

Germany

Copyright

ACM

hierarchy

and

the

pagers

lexical

chains

for

measuring

the

pages

topic-importance

and

semantic

similarities

values

Finally

we

present

how

our

DirectoryRank

metric

employs

the

above

values

for

measuring

how

informative

Web

pages

are

with

respect

to

some

topics

and

rank

them

accordingly

In

Section

we

experimentally

study

the

effectiveness

of

DirectoryRank

by

comparing

its

performance

to

PageRank

We

revise

related

work

in

Section

and

we

conclude

the

paper

in

Section

In

this

section

we

briefly

explain

the

main

intuition

of

PageRank

a

metric

that

was

primarily

invented

for

ranking

pages

within

the

Google

Search

Engine

and

that

is

currently

used

within

the

Google

Directory

for

ordering

Web

pages

For

a

more

elaborate

overview

on

PageRank

we

refer

the

reader

to

the

work

of

-LSB-

-RSB-

The

intuition

of

PageRank

metric

is

that

a

page

on

the

Web

is

important

if

there

are

a

lot

of

other

important

pages

pointing

to

it

That

is

if

a

page

p

has

many

links

from

other

important

pages

we

may

conclude

that

this

page

is

interesting

to

many

people

and

that

it

should

be

considered

as

being

important

or

of

good

quality

Similarly

if

an

important

page

has

links

to

other

pages

we

expect

that

part

of

its

quality

is

transferred

to

the

pages

it

points

to

which

in

turn

become

of

increased

significance/quality

Roughly

PageRank

PR

-LRB-

p

-RRB-

defines

the

importance

of

page

p

to

be

the

sum

of

the

importance

of

the

pages

that

endorse

p

At

a

high

level

PageRank

is

calculating

the

probability

that

a

random

surfer

is

looking

at

a

given

page

at

a

given

point

of

time

The

random

surfer

is

a

mathematical

model

that

emulates

the

behavior

of

a

user

that

given

a

page

follows

an

outgoing

link

from

that

page

at

random

Formally

given

a

page

p

i

that

has

incoming

links

from

the

pages

p

p

n

and

let

c

j

be

the

number

of

out-links

from

p

j

the

PageRank

of

p

i

is

given

by

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

i

n

n

PR

p

d

d

PR

p

c

PR

p

c

where

d

corresponds

to

the

probability

that

the

random

surfer

will

get

bored

and

his

next

visit

will

be

a

completely

random

page

and

corresponds

to

the

probability

that

the

page

the

random

surfer

will

pick

for

his

next

visit

is

an

outgoing

link

of

the

current

page

The

ranking

of

a

Web

page

within

a

particular

topic

intuitively

depends

on

two

criteria

-LRB-

i

-RRB-

the

importance

of

the

page

for

the

underlying

topic

This

criterion

helps

us

identify

the

most

important

pages

out

of

the

several

ones

that

may

lie

within

a

particular

topic

-LRB-

ii

-RRB-

the

semantic

correlation

of

a

page

relative

to

other

important

pages

in

the

same

topic

This

criterion

helps

us

rank

pages

relative

to

each

other

within

a

topic

For

measuring

the

importance

of

a

Web

page

in

some

topic

we

explore

a

subject

hierarchy

that

we

have

built

in

the

course

of

an

earlier

study

-LSB-

-RSB-

and

we

use

the

lexical

chaining

technique

for

identifying

the

most

important

words

inside

the

page

We

start

our

discussion

with

a

presentation

of

the

topic

hierarchy

that

we

use

in

our

work

-LRB-

Section

-RRB-

and

we

describe

the

process

we

follow

for

representing

Web

pages

into

lexical

chains

-LRB-

Section

-RRB-

We

also

explain

how

we

utilize

the

topic

hierarchy

and

the

pages

lexical

chains

for

measuring

the

pages

importance

to

the

hierarchy

's

topics

-LRB-

Section

-RRB-

The

contribution

of

our

work

lies

in

the

exploitation

of

the

topic

hierarchy

and

the

lexical

chains

that

we

generate

for

representing

Web

pages

in

order

to

compute

the

semantic

similarities

between

the

pages

that

are

important

in

some

topics

Moreover

we

have

developed

a

novel

framework

which

employs

the

pages

topic

importance

and

semantic

similarity

measures

for

ranking

pages

inside

Directory

topics

The

Topic

Hierarchy

The

main

intuition

in

our

DirectoryRank

metric

is

that

topic

relevance

estimation

of

a

Web

page

relies

on

the

page

's

lexical

coherence

i.e.

having

a

substantial

portion

of

words

associated

with

the

same

topic

To

capture

this

property

we

adopt

the

lexical

chaining

approach

for

every

Web

page

we

generate

a

sequence

of

semantically

related

terms

known

as

lexical

chain

In

our

approach

of

representing

Web

pages

into

lexical

chains

we

adopt

the

method

reported

in

-LSB-

-RSB-

which

uses

WordNet

-LSB-

-RSB-

as

the

knowledge

base

for

providing

information

about

the

semantic

relations

that

exist

between

the

words

in

a

text

A

detailed

description

of

the

lexical

chains

generation

process

is

given

in

Section

Before

that

we

present

the

topic

hierarchy

that

we

use

for

determining

the

topics

that

are

associated

with

the

contents

-LRB-

i.e.

words

-RRB-

of

Web

pages

Since

we

are

mainly

interested

in

measuring

the

Web

pages

importance

in

the

context

of

Web

Directories

we

decided

to

demonstrate

the

usefulness

of

our

DirectoryRank

metric

in

ordering

Web

pages

in

the

topics

currently

used

in

a

real

Web

Directory

To

that

end

we

applied

DirectoryRank

to

the

main

topics

used

in

the

Google

Directory

Google

Directory

provides

a

hierarchical

listing

of

Web

pages

categorized

by

topic

and

reuses

the

data

maintained

by

the

Open

Directory

Project

Moreover

since

DirectoryRank

relies

on

the

Web

pages

lexical

chains

rather

than

their

entire

contents

for

measuring

the

pages

importance

to

particular

topics

and

since

lexical

chain

generation

is

dependent

on

WordNet

we

decided

to

enrich

the

top

level

-LRB-

main

-RRB-

topics

of

the

Google

Directory

with

their

respective

WordNet

lexical

hierarchies

The

first

step

we

took

for

enriching

the

Google

topics

with

WordNet

data

was

to

examine

the

compatibility

between

these

topics

and

the

topics

used

to

annotate

WordNet

's

concepts

with

domain

information

Note

that

the

topic

information

that

exists

in

the

labels

of

WordNet

's

contents

is

taken

from

the

freely

available

Suggested

Upper

Merged

Ontology

-LRB-

SUMO

-RRB-

-LSB-

-RSB-

and

the

MultiWordNet

Domains

-LRB-

MWND

-RRB-

-LSB-

-RSB-

Due

to

space

limitations

here

we

present

a

summary

of

our

approach

into

enriching

the

Google

topics

with

WordNet

hierarchies

A

detailed

description

of

the

process

we

followed

for

appending

to

the

Google

top

level

topics

their

corresponding

WordNet

hierarchies

is

given

in

-LSB-

-RSB-

In

brief

we

located

the

Google

's

top

level

topics

among

the

topics

used

in

either

SUMO

or

MWND

for

annotating

WordNet

concepts

Out

of

the

Google

topics

topics

-LRB-

shown

in

Table

-RRB-

are

used

for

labeling

WordNet

concepts

with

topic

information

To

each

of

those

topics

we

integrated

their

corresponding

sub-topics

that

we

ac-quired

from

either

SUMO

or

MWND

The

sub-topic

integration

was

performed

automatically

simply

by

following

WordNet

's

hyper/hyponymy

links

At

the

end

of

this

process

we

came

down

to

a

hierarchy

of

sub-topics

which

are

organized

into

the

top

level

topics

that

we

used

from

Google

Directory

Table

The

Hierarchy

's

First

Level

Topics

First

Level

Topics

Arts

News

Sports

Society

Games

Computers

Home

Reference

Shopping

Recreation

Business

Science

Health

In

Section

we

will

demonstrate

how

to

use

our

topic

hierarchy

for

automating

the

task

of

ranking

pages

within

topical

categories

Measuring

Web

Pages

Topic

Importance

The

computational

model

that

we

adopted

for

generating

lexical

chains

is

presented

in

the

work

of

Barzilay

-LSB-

-RSB-

and

it

generates

lexical

chains

in

a

three

step

approach

-LRB-

i

-RRB-

it

selects

a

set

of

candidate

terms

from

a

page

-LRB-

ii

-RRB-

for

each

candidate

term

it

finds

an

appropriate

chain

relying

on

a

relatedness

criterion

among

members

of

the

chains

and

-LRB-

iii

-RRB-

if

such

a

chain

is

found

it

inserts

the

term

in

the

chain

The

relatedness

factor

in

the

second

step

is

determined

by

the

type

of

WordNet

links

that

connect

the

candidate

term

to

the

terms

stored

in

existing

lexical

chains

Figure

illustrates

an

example

of

the

lexical

chain

generated

for

a

text

containing

the

candidate

terms

system

network

sensor

weapon

missile

surface

and

net

The

subscript

si

denotes

the

id

of

the

word

's

sense

within

WordNet

Lexical

chain

system

network

system

sensor

system

weapon

missile

system

surface

net

Figure

An

example

of

a

lexical

chain

Having

generated

lexical

chains

we

disambiguate

the

sense

of

the

words

inside

every

chain

by

employing

the

scoring

function

f

introduced

in

-LSB-

-RSB-

which

indicates

the

probability

that

a

word

relation

is

a

correct

one

Given

two

words

w

and

w

their

scoring

function

f

via

a

relation

r

depends

on

the

words

association

score

their

depth

in

WordNet

and

their

respective

relation

weight

The

association

score

-LRB-

Assoc

-RRB-

of

the

word

pair

-LRB-

w

w

-RRB-

is

determined

by

the

words

co-occurrence

frequency

in

a

corpus

that

has

been

previously

collected

In

practice

the

greater

the

association

score

between

a

word

pair

w

and

w

is

the

greater

the

likelihood

that

w

and

w

refer

to

the

same

topic

Formally

the

-LRB-

Assoc

-RRB-

score

of

the

word

pair

-LRB-

w

w

-RRB-

is

given

by

log

-LRB-

-LRB-

-RRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

s

s

p

w

w

Assoc

w

w

N

w

N

w

where

p

-LRB-

w

w

-RRB-

is

the

corpus

co-occurrence

probability

of

the

word

pair

-LRB-

w

w

-RRB-

and

N

s

-LRB-

w

-RRB-

is

a

normalization

factor

which

indicates

the

number

of

WordNet

senses

that

a

word

w

has

Given

a

word

pair

-LRB-

w

w

-RRB-

their

DepthScore

expresses

the

words

position

in

WordNet

hierarchy

and

is

defined

as

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

DepthScore

w

w

Depth

w

Depth

w

where

Depth

-LRB-

w

-RRB-

is

the

depth

of

word

w

in

WordNet

Semantic

relation

weights

-LRB-

RelationWeight

-RRB-

have

been

experimentally

fixed

to

for

reiteration

for

synonymy

and

hyper/hyponymy

for

antonymy

for

mero/holonymy

and

for

siblings

The

scoring

function

f

of

w

and

w

is

defined

as

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

Re

-LRB-

-RRB-

s

f

w

w

r

Assoc

w

w

DepthScore

w

w

lationWeight

r

The

value

of

the

function

f

represents

the

probability

that

the

relation

type

r

is

the

correct

one

between

words

w

and

w

In

order

to

disambiguate

the

senses

of

the

words

within

lexical

chain

C

i

we

calculate

its

score

by

summing

up

the

f

s

scores

of

all

the

words

w

w

-LRB-

where

w

and

w

are

successive

words

-RRB-

within

the

chain

C

i

Formally

the

score

of

lexical

chain

C

i

is

expressed

as

the

sum

of

the

score

of

each

relation

r

j

in

C

i

-LRB-

-RRB-

-LRB-

-RRB-

i

s

j

j

j

r

in

C

j

j

Score

C

f

w

w

r

As

candidate

terms

we

use

nouns

and

proper

names

because

they

convey

the

vast

majority

of

conceptual

information

in

texts

Eventually

in

order

to

disambiguate

we

will

pick

the

relations

and

senses

that

maximize

the

Score

-LRB-

C

i

-RRB-

for

that

particular

chain

In

estimating

the

importance

of

a

Web

page

p

i

in

some

Directory

's

topic

T

k

our

first

step

is

to

identify

which

node

within

the

hierarchy

-LRB-

see

Section

-RRB-

corresponds

to

topic

T

k

of

the

page

Topic-Importance

Scoring

Once

the

topic

of

a

page

is

located

among

the

hierarchy

's

topics

we

map

the

words

in

the

page

's

lexical

chain

to

the

WordNet

nodes

under

that

particular

topic

Recall

that

upon

lexical

chain

generation

words

are

disambiguated

ensuring

that

every

word

inside

a

page

is

mapped

to

a

single

word

within

the

WordNet

hierarchy

We

then

determine

the

importance

of

a

page

p

i

to

topic

T

k

by

counting

the

number

of

words

in

the

lexical

chain

of

p

i

that

are

subsumed

by

T

k

in

the

hierarchy

's

graph

The

topic

importance

of

a

page

is

given

by

a

Relatedness

Score

-LRB-

RScore

-RRB-

which

indicates

how

relevant

a

page

is

for

a

given

topic

Formally

the

relatedness

score

of

a

page

p

i

-LRB-

represented

by

the

lexical

chain

C

i

-RRB-

to

the

hierarchy

's

topic

T

k

is

defined

as

the

product

of

the

page

's

chain

Score

-LRB-

C

i

-RRB-

and

the

fraction

of

words

in

the

page

's

chain

that

are

descendants

of

T

k

Formally

the

RScore

is

given

by

RScore

-LRB-

C

i

T

k

-RRB-

k

i

i

i

Score

-LRB-

C

-RRB-

common

C

and

T

elements

C

elements

The

denominator

is

used

to

remove

any

effect

the

length

of

a

lexical

chain

might

have

on

RScore

and

ensures

that

the

final

score

is

normalized

so

that

all

values

are

between

and

with

corresponding

to

no

relatedness

at

all

and

indicating

the

page

that

is

highly

expressive

of

the

page

's

topic

The

RScore

of

a

page

to

a

specific

topic

captures

the

importance

of

the

page

in

the

given

topic

Semantic

Similarity

Scoring

The

relatedness

score

metric

that

we

have

just

presented

can

serve

as

a

good

indicator

for

identifying

the

most

important

pages

within

a

topic

However

the

RScore

metric

does

not

capture

the

amount

of

common

content

that

is

shared

between

the

Web

pages

in

a

topic

This

is

important

in

the

cases

where

our

topic-importance

scoring

gives

a

low

score

for

some

pages

but

at

the

same

time

these

pages

are

very

similar

to

other

pages

with

high

topic-importance

scores

In

order

to

accommodate

for

this

scenario

we

now

show

how

to

compute

the

semantic

similarities

among

the

pages

that

are

listed

in

the

same

Directory

topic

Semantic

similarity

is

indicative

of

the

pages

semantic

correlation

and

helps

in

determining

the

ordering

of

the

pages

that

are

deemed

important

in

some

topic

Our

DirectoryRank

metric

employs

the

Web

page

's

topic-importance

scores

and

their

semantic

similarities

to

determine

their

ranking

order

inside

some

Directory

topics

and

is

presented

in

the

next

section

In

order

to

estimate

the

Web

pages

semantic

similarity

we

compare

the

elements

in

a

page

's

lexical

chain

to

the

elements

in

the

lexical

chains

of

the

other

pages

in

a

Directory

topic

We

assume

that

if

the

chains

of

two

Web

pages

have

a

large

number

of

elements

in

common

then

the

pages

are

correlated

to

each

other

To

compute

similarities

between

pages

p

i

and

p

j

that

are

categorized

in

the

same

topic

we

first

need

to

identify

the

common

elements

between

their

lexical

chains

represented

as

PC

i

and

PC

j

respectively

First

we

use

WordNet

to

augment

the

elements

of

the

lexical

chains

PC

i

and

PC

j

with

their

synonyms

Chain

augmentation

ensures

that

pages

of

comparable

content

are

not

regarded

unrelated

if

their

lexical

chains

contain

distinct

but

semantically

equivalent

elements

The

augmented

elements

of

PC

i

and

PC

j

are

defined

as

-LRB-

-RRB-

-LRB-

-RRB-

i

i

i

AugElements

PC

C

Synonyms

C

U

-LRB-

-RRB-

-LRB-

-RRB-

j

j

j

AugElements

PC

C

Synonyms

C

U

where

Synonyms

-LRB-

C

i

-RRB-

denotes

the

set

of

the

hierarchy

's

concepts

that

are

synonyms

to

any

of

the

elements

in

C

i

and

Synonyms

-LRB-

C

j

-RRB-

denotes

the

set

of

the

hierarchy

's

concepts

that

are

synonyms

to

any

of

the

elements

in

C

j

The

common

elements

between

the

augmented

lexical

chains

PC

i

and

PC

j

are

determined

as

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

i

j

i

j

ComElements

PC

PC

AugElements

PC

AugElements

PC

I

We

formally

define

the

problem

of

computing

pages

semantic

similarities

as

follows

if

the

lexical

chains

of

pages

p

i

and

p

j

share

elements

in

common

we

produce

the

correlation

look

up

table

with

tuples

of

the

form

AugElements

-LRB-

PC

i

-RRB-

AugElements

-LRB-

PC

j

-RRB-

ComE-lements

gt

The

similarity

measurement

between

the

lexical

chains

PC

i

PC

j

of

the

pages

P

i

and

P

j

is

given

by

-RRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-LRB-

i

j

i

j

i

j

ComElements

PC

PC

PC

PC

s

AugElements

PC

AugElements

PC

where

the

degree

of

semantic

similarity

is

normalized

so

that

all

values

are

between

zero

and

one

with

indicating

that

the

two

pages

are

totally

different

and

indicating

that

the

two

pages

talk

about

the

same

thing

DirectoryRank

Scoring

Pages

are

sorted

in

Directory

topics

on

the

basis

of

a

DirectoryRank

metric

which

defines

the

importance

of

the

pages

with

respect

to

the

particular

topics

in

the

Directory

DirectoryRank

-LRB-

DR

-RRB-

measures

the

quality

of

a

page

in

some

topic

by

the

degree

to

which

the

page

correlates

to

other

informative/qualitative

pages

in

the

given

topic

Intuitively

an

informative

page

in

a

topic

is

a

page

that

has

a

high

relatedness

score

to

the

Directory

's

topic

and

that

is

semantically

close

-LRB-

similar

-RRB-

to

many

other

pages

in

that

topic

DR

defines

the

quality

of

a

page

to

be

the

sum

of

its

topic

relatedness

score

and

its

overall

similarity

to

the

fraction

of

pages

with

which

it

correlates

in

the

given

topic

This

way

if

a

page

is

highly

related

to

topic

D

and

also

correlates

highly

with

many

informative

pages

in

D

its

DR

score

will

be

high

Formally

consider

that

page

p

i

is

indexed

in

Directory

topic

T

k

with

some

RScore

-LRB-

p

i

T

k

-RRB-

and

let

p

p

p

n

be

pages

in

T

k

with

which

p

i

semantically

correlates

with

scores

of

s

-LRB-

PC

PC

i

-RRB-

s

-LRB-

PC

PC

i

-RRB-

s

-LRB-

PC

n

PC

i

-RRB-

respectively

Then

the

DirectoryRank

-LRB-

DR

-RRB-

of

p

i

is

given

by

-LRB-

-RRB-

-LRB-

-RRB-

-LSB-

-LRB-

-RRB-

-LRB-

-RRB-

-LRB-

-RRB-

-RSB-

i

k

i

k

s

i

s

i

s

n

DR

p

T

RScore

p

T

PC

PC

PC

PC

PC

PC

n

i

where

n

corresponds

to

the

total

number

of

pages

in

topic

T

k

with

which

p

i

semantically

correlates

To

measure

the

potential

of

our

DirectoryRank

metric

in

delivering

topic-informative

rankings

we

conducted

an

experiment

where

we

studied

the

effectiveness

of

DR

in

prioritizing

the

most

informative

pages

in

some

Directory

's

topics

To

obtain

perceptible

evidence

of

DirectoryRank

's

efficiency

in

a

practical

setting

we

applied

our

DR

metric

to

a

set

of

Web

pages

listed

in

a

number

of

topics

in

Google

Directory

and

we

compared

the

rankings

induced

by

DirectoryRank

to

the

rankings

that

Google

Directory

delivers

for

the

same

set

of

pages

and

topics

In

Section

we

explain

how

we

selected

the

pages

for

our

study

while

in

Section

we

present

the

similarity

measure

that

we

used

for

comparing

the

rankings

induced

by

DirectoryRank

to

the

rankings

delivered

by

PageRank

and

we

give

obtained

results

Moreover

to

delve

into

the

behavior

of

DirectoryRank

we

carried

out

a

user

study

presented

in

Section

Experimental

Dataset

In

selecting

our

experimental

data

we

picked

pages

that

are

categorized

in

those

topics

in

Google

Directory

which

are

also

present

in

our

hierarchy

Recall

that

Google

Directory

is

a

replica

of

the

Dmoz

Directory

from

which

we

borrowed

our

hierarchy

's

top-level

topics

Out

of

all

the

sub-topics

organized

in

those

top-level

topics

in

Google

Directory

were

represented

in

our

hierarchy

Having

determined

the

topics

whose

set

of

ranked

pages

would

be

compared

we

downloaded

a

total

number

of

pages

categorized

in

one

of

the

selected

topics

which

in

turn

are

organized

into

the

top-level

topics

Table

shows

the

distribution

of

the

experimental

pages

in

the

top

level

topics

in

Google

Directory

Table

Statistics

on

the

experimental

data

Category

of

documents

of

sub-topics

Arts

Sports

Games

Home

Shopping

Business

Health

News

Society

Computers

Reference

Recreation

Science

Total

Since

we

were

interested

in

comparing

DirectoryRank

with

PageRank

in

the

context

of

ranking

Web

pages

in

Directory

topics

we

recorded

for

the

downloaded

Web

pages

their

relative

ranking

order

in

Google

Directory

in

each

of

the

selected

topics

We

then

stored

the

downloaded

pages

in

a

secondary

index

maintaining

their

relative

PageRank

rankings

To

compute

the

DR

values

for

every

experimental

page

we

initially

processed

the

downloaded

pages

in

order

to

generate

and

score

their

lexical

chains

For

every

page

we

first

computed

its

RScore

to

the

topic

in

which

it

is

assigned

in

Google

Directory

and

then

we

computed

the

semantic

similarity

-LRB-

s

-RRB-

for

every

pair

of

pages

listed

in

each

topic

Lastly

using

the

above

two

scores

-LRB-

i.e.

semantic

similarity

and

topic

relatedness

-RRB-

we

computed

for

every

Web

page

its

DirectoryRank

-LRB-

DR

-RRB-

value

and

we

sorted

the

pages

listed

within

each

of

the

topics

so

that

pages

with

higher

DR

scores

in

some

topic

are

prioritized

among

the

set

of

topic

related

pages

Using

the

above

data

we

evaluated

the

effectiveness

of

our

DirectoryRank

metric

in

ordering

Web

pages

inside

the

Directory

's

topics

Overlap

of

DirectoryRank

and

PageRank

To

investigate

whether

there

is

any

similarity

between

the

rankings

induced

by

DirectoryRank

and

the

rankings

delivered

by

PageRank

for

our

experimental

pages

in

the

topics

in

Google

Directory

we

used

the

OSim

measure

reported

in

the

work

of

-LSB-

-RSB-

which

indicates

the

degree

of

overlap

between

the

top

n

URLs

of

the

two

rankings

Formally

the

overlap

of

two

ranked

lists

A

and

B

-LRB-

each

of

size

n

-RRB-

is

given

by

-LRB-

-RRB-

OSim

DR

PR

A

B

n

I

Using

the

above

formula

we

computed

for

each

of

the

topics

the

overlap

between

the

pages

ranked

in

the

top

n

positions

for

that

topic

by

DR

and

PR

respectively

Afterwards

we

first

puted

the

average

similarity

between

the

two

induced

rankings

for

each

of

the

selected

topics

and

then

the

average

similarity

between

the

two

induced

rankings

for

each

of

the

top-level

topics

To

compute

the

average

similarity

between

DR

and

PR

for

a

top

level

topic

T

we

summed

the

average

similarity

of

all

sub-topics

in

T

and

we

divided

by

the

number

of

sub-topics

that

T

has

Table

gives

the

average

similarity

scores

between

DR

and

PR

for

each

of

the

top-level

topics

examined

in

our

experiment

Table

Average

similarity

of

rankings

for

the

top

level

topics

Category

OSim

Arts

Sports

Games

Home

Shopping

Business

Health

News

Society

Computers

Reference

Recreation

Science

Obtained

results

demonstrate

that

there

is

little

average

overlap

between

the

top

results

for

the

two

rankings

Note

that

for

some

topics

we

compared

the

overlap

between

DR

and

PR

for

a

larger

set

of

pages

-LRB-

e.g.

n

and

n

-RRB-

and

we

found

that

the

OSim

score

of

the

two

rankings

increases

albeit

slightly

as

the

size

of

n

grows

For

example

in

the

topic

Sports

the

OSim

between

DR

and

PR

for

n

is

whereas

for

n

the

OSim

score

is

and

for

n

OSim

is

Our

results

show

that

even

the

pairs

with

the

greatest

similarity

among

all

pairs

examined

-LRB-

e.g.

the

rankings

delivered

for

the

News

topic

-RRB-

according

to

the

OSim

measure

have

little

in

common

Despite

the

usefulness

of

the

OSim

measure

for

making

rough

estimations

about

the

ability

of

the

two

ranking

schemes

in

identifying

the

same

top

pages

with

respect

to

some

topics

it

can

not

directly

capture

which

ranking

is

more

useful

for

ordering

pages

in

Directory

topics

This

is

because

OSim

does

not

indicate

the

degree

to

which

the

relative

orderings

of

the

top

n

pages

of

two

rankings

are

in

agreement

Having

established

that

PageRank

and

DirectoryRank

order

Web

pages

substantially

differ-ently

we

proceed

to

investigate

which

of

these

rankings

is

better

for

ordering

Web

pages

in

Directory

topics

To

that

end

we

carried

out

a

user

study

reported

next

DirectoryRank

Performance

To

determine

which

of

the

two

ranking

measures

namely

DR

and

PR

is

perceived

as

more

useful

by

Web

users

for

organizing

pages

in

Web

Directories

we

carried

out

a

user

study

From

our

sample

data

we

picked

the

top

pages

listed

in

randomly

selected

topics

-LRB-

out

of

the

topics

examined

-RRB-

and

we

recruited

postgraduate

volunteers

from

our

school

Table

lists

the

topics

selected

For

each

topic

the

volunteer

was

shown

result

rankings

one

consisted

of

the

top

pages

for

the

topic

ranked

with

DR

and

the

other

consisted

of

the

top

pages

for

the

topic

ranked

with

PR

For

each

topic

the

volunteer

was

asked

to

read

the

pages

in

both

lists

and

indicate

which

of

the

two

rankings

in

their

opinion

is

more

useful

overall

for

communicating

information

about

the

topic

Volunteers

were

not

told

anything

about

how

either

of

the

rankings

was

generated

In

order

to

avoid

misinterpretations

while

analyzing

the

user

's

selection

preferences

we

asked

from

the

users

to

indicate

their

descriptive

selections

directly

More

specifically

we

presented

to

our

participants

the

following

choices

and

we

asked

them

to

indicate

for

which

of

the

following

reasons

they

selected

one

ranking

over

the

other

for

each

of

the

topics

examined

Table

Experimental

Topics

Experimental

Topics

T

Crime

T

Photography

T

Water

Sports

T

Radiology

T

Mechanics

T

Econometrics

T

Collecting

Reason

I

prefer

this

ranking

because

I

obtained

significant

information

about

the

topic

from

most

of

the

pages

In

our

analysis

we

interpret

the

ranking

preferences

established

on

this

reason

as

topic-informative

rankings

Reason

I

prefer

this

ranking

because

I

have

seen

most

of

the

pages

before

and

I

liked

them

We

interpret

the

ranking

preferences

established

on

this

reason

as

popular

rankings

We

then

compared

the

participants

descriptive

selections

for

every

topic

with

the

final

DR

PR

choices

This

way

we

ensure

that

users

preferences

would

be

accurately

evaluated

even

if

two

volunteers

had

exactly

the

same

descriptive

selection

but

they

ended

up

casting

that

selection

into

different

DR

PR

rankings

As

a

final

note

we

also

asked

our

volunteers

to

indicate

their

familiarity

with

the

experimental

topics

by

characterizing

as

familiar

or

unfamiliar

each

of

the

topics

examined

In

our

evaluation

we

considered

that

one

ranking

was

better

than

the

other

if

at

least

of

the

users

selected

it

as

more

useful

Table

shows

the

rankings

selected

by

our

subjects

as

more

useful

for

each

of

the

examined

topics

Every

row

corresponds

to

a

separate

user

The

columns

marked

as

T

i

show

what

the

preference

of

the

user

was

for

the

particular

topic

Under

the

T

i

columns

the

keyword

DR

means

that

the

user

considered

DirectoryRank

as

more

useful

for

that

topic

while

PR

means

that

the

user

deemed

PageRank

as

more

useful

The

column

marked

as

R

on

the

right

of

a

T

i

column

indicates

the

reason

for

which

the

user

voted

over

the

specified

ranking

Table

summarizes

the

rankings

preferred

by

the

majority

of

the

users

for

each

of

the

topics

Table

Rankings

selected

as

more

useful

for

each

topic

User

T

R

T

R

T

R

T

R

T

R

T

R

T

R

DR

DR

DR

DR

PR

DR

PR

PR

DR

PR

DR

DR

DR

PR

DR

DR

DR

DR

DR

DR

PR

PR

PR

PR

DR

PR

PR

PR

DR

PR

PR

PR

PR

DR

DR

PR

DR

PR

DR

DR

DR

DR

DR

PR

PR

DR

PR

DR

DR

DR

DR

DR

DR

PR

DR

PR

PR

DR

PR

PR

PR

DR

DR

DR

DR

DR

DR

DR

DR

DR

DR

DR

DR

DR

PR

PR

PR

DR

DR

DR

PR

PR

DR

DR

DR

PR

PR

DR

PR

DR

DR

PR

DR

PR

DR

DR

DR

PR

DR

DR

DR

DR

PR

DR

DR

Our

survey

results

demonstrate

that

the

majority

of

the

users

perceived

in

overall

DirectoryRank

as

more

useful

in

comparison

to

PageRank

for

ordering

Web

pages

in

the

Directory

's

topics

This

is

attested

by

the

fact

that

for

most

of

the

topics

examined

-LRB-

for

out

of

the

topics

-RRB-

the

majority

of

our

subjects

preferred

DR

over

PR

A

closer

look

at

the

obtained

results

indicates

that

the

reason

on

which

our

participants

based

most

of

their

DR

selections

is

Reason

which

implies

that

the

rankings

delivered

by

DR

are

perceived

as

more

topic-informative

Conversely

most

of

the

users

who

liked

better

the

rankings

induced

by

PR

established

their

selection

on

Reason

This

suggests

that

the

usefulness

of

PR

is

not

implied

mainly

by

how

informative

a

page

is

about

a

topic

but

rather

that

it

is

substantially

influenced

by

the

page

's

popularity

Table

Rankings

preferred

by

the

majority

of

users

Topic

Preferred

by

majority

T

Crime

DirectoryRank

T

Photography

DirectoryRank

T

Water

Sports

PageRank

T

Radiology

DirectoryRank

T

Mechanics

PageRank

T

Econometrics

DirectoryRank

T

Collecting

DirectoryRank

Moreover

although

not

reported

here

due

to

space

limit

our

survey

results

show

that

our

participants

answers

were

not

generally

influenced

by

their

familiarity

or

not

with

the

underlying

topics

This

implies

that

our

survey

does

not

entail

topic-bias

since

both

rankings

compared

are

applied

to

pages

listed

in

the

same

topic

There

have

been

a

number

of

studies

trying

to

identify

the

best

ranking

order

of

the

Web

pages

that

are

deemed

to

be

relevant

to

a

given

query/topic

The

most

successful

of

these

studies

-LSB-

-RSB-

suggest

the

exploitation

of

the

pages

links

connectivity

on

the

Web

graph

for

measuring

the

pages

importance

and

rank

them

accordingly

The

most

widely

known

ranking

metric

that

explores

the

pages

links

structure

for

measuring

their

importance

on

the

Web

is

PageRank

Currently

PageRank

and

its

variations

are

used

by

most

major

Web

Search

Engines

to

rank

the

results

that

they

return

to

Web

users

in

response

to

their

search

requests

Despite

PageRank

's

usefulness

for

ordering

pages

in

the

context

of

Search

Engines

it

is

designed

to

measure

the

global

importance

of

the

pages

on

the

Web

independent

of

any

particular

topics

However

the

overall

importance

of

the

pages

may

be

not

a

sufficient

measure

for

ordering

the

pages

inside

Directories

topics

essentially

because

pages

that

are

important

in

some

topics

may

not

be

important

in

others

regardless

of

the

number

and

structure

of

the

links

that

may

appear

in

those

pages

To

alleviate

some

of

the

inherent

limitations

of

PageRank

a

number

of

researchers

designed

new

ranking

metrics

which

mainly

rely

on

modifications

of

PageRank

and

are

tailored

for

specific

tasks

For

example

-LSB-

-RSB-

studies

personalization

of

the

PageRank

metric

by

giving

different

weights

to

pages

-LSB-

-RSB-

examine

the

local

and

the

inter-site

link

structure

in

order

to

compute

a

global

PageRank

for

Web

pages

-LSB-

-RSB-

introduce

Hilltop

an

algorithm

which

generates

query-specific

authority

scores

for

improving

rankings

for

popular

queries

While

most

of

these

works

mainly

focus

on

improving

the

rankings

delivered

to

Web

users

by

measuring

the

Web

pages

overall

importance

in

this

paper

we

are

more

concerned

about

the

topic

importance

of

Web

pages

by

measuring

the

pages

informativeness

with

respect

to

particular

topics

In

this

scope

we

perceive

our

work

to

be

complementary

to

previous

studies

on

personalized

rankings

-LSB-

-RSB-

Moreover

there

exists

prior

work

that

explores

the

lexical

chaining

technique

as

a

means

for

representing

documents

contents

-LSB-

-RSB-

Recently

we

employed

the

lexical

chaining

technique

for

the

automatic

classification

of

Web

documents

in

topic

hierarchies

-LSB-

-RSB-

Our

findings

indicated

the

potential

of

lexical

chains

in

successfully

capturing

the

thematic

content

of

Web

pages

This

motivated

our

work

to

use

the

lexical

chains

generated

for

a

number

of

Web

pages

as

a

means

for

ordering

pages

within

Directory

topics

In

the

future

we

plan

to

investigate

how

our

approach

could

benefit

from

other

linguistic

approaches

besides

lexical

chains

In

this

paper

we

introduced

DirectoryRank

a

practical

metric

for

determining

how

informative

Web

pages

are

for

particular

topics

and

ranking

them

accordingly

To

evaluate

the

potential

of

DirectoryRank

in

ordering

Web

pages

inside

Directory

topics

we

conducted

an

experiment

where

we

applied

our

DirectoryRank

metric

to

order

a

set

of

pages

listed

within

topics

in

Google

Directory

and

we

compared

the

rankings

induced

by

DirectoryRank

to

the

rankings

that

PageRank

delivers

in

Google

Directory

for

the

same

set

of

pages

and

topics

In

our

study

we

relied

on

the

judgments

made

by

users

to

determine

which

ranking

is

perceived

as

more

useful

for

Web

Directories

users

Obtained

results

indicate

that

in

overall

users

preferred

DirectoryRank

over

PageRank

for

ordering

Web

pages

inside

the

Directory

's

topics

Although

it

would

probably

require

additional

studies

in

order

to

evaluate

the

applicability

of

our

method

to

Web

Directories

other

than

Google

and

assess

DirectoryRank

's

usefulness

to

a

larger

user

and

categories

base

we

believe

that

our

work

can

serve

as

the

first

step

towards

a

topic-informative

ranking

metric

within

directories

-LSB-

-RSB-

Google

Directory

-LSB-

-RSB-

MultiWordNet

Domains

-LSB-

-RSB-

Open

Directory

Project

-LSB-

-RSB-

Sumo

Ontology

-LSB-

-RSB-

WordNet

-LSB-

-RSB-

Barzilay

R

Lexical

chains

for

text

summarization

Master

's

Thesis

Ben-Gurion

University

-LSB-

-RSB-

Bharat

K

and

Mihaila

G.

Hilltop

a

search

engine

based

on

expert

documents

hilltop

-LSB-

-RSB-

Kleinberg

J.

Authoritative

sources

in

a

hyperlinked

environment

In

Journal

of

the

ACM

-LRB-

-RRB-

-LSB-

-RSB-

Haveliwala

T.

Topic

sensitive

PageRank

In

Proceedings

of

the

th

WWW

Conference

-LSB-

-RSB-

Ntoulas

A.

Cho

J.

and

Olston

Ch

What

's

new

on

the

web

The

evolution

of

the

web

from

a

search

engine

perspective

In

Proceedings

of

the

th

WWW

Conference

-LSB-

-RSB-

Page

L.

Brin

S.

Motwani

R.

and

Winograd

T

The

PageRank

citation

ranking

Bringing

order

to

the

web

Available

at

-LSB-

-RSB-

Song

Y.I.

Han

K.S.

and

Rim

H.C.

A

term

weighting

method

based

on

lexical

chain

for

automatic

summarization

In

Proceedings

of

the

th

CICLing

Conference

-LSB-

-RSB-

Stamou

S.

Krikos

V.

Kokosis

P.

Ntoulas

A.

and

Christodoulakis

D.

Web

directory

construction

using

lexical

chains

In

Proceedings

of

the

th

NLDB

Conference

-LSB-

-RSB-

Wang

Y.

and

DeWitt

D.

Computing

PageRank

in

a

distributed

internet

search

system

In

Proc

of

the

th

VLDB

Conf.

B O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O B I O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O B O O O O B I O O O O O B I O O O O O O O O B O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O B I O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O B I O B I O O B O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B I O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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O O O B O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O B O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O B I O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O

semantic similarities
scoring
lexical chains
directoryrank
ranking
web directory
semantic similarity
topic importance
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information retrieval
topic hierachy
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Contour-based Partial Object Recognition using Symmetry in Image Databases This paper discusses the problem of partial object recognition in image databases . We propose the method to reconstruct and estimate partially occluded shapes and regions of objects in images from overlapping and cutting . We present the robust method for recognizing partially occluded objects based on symmetry properties , which is based on the contours of objects . Our method provides simple techniques to reconstruct occluded regions via a region copy using the symmetry axis within an object . Based on the estimated parameters for partially occluded objects , we perform object recognition on the classification tree . Since our method relies on reconstruction of the object based on the symmetry rather than statistical estimates , it has proven to be remarkably robust in recognizing partially occluded objects in the presence of scale changes , rotation , and viewpoint changes . Most existing methods for object recognition are based on full objects . However , many images in electronic catalogs contain multiple objects with occluded shapes and regions . Due to the occlusion of objects , image retrieval can provide incomplete , uncertain , and inaccurate results . To resolve this problem , we propose new method to reconstruct objects using symmetry properties since most objects in a given image database are represented by symmetrical figures . Even though there have been several efforts in object recognition with occlusion , currents methods have been highly sensitive to object pose , rotation , scaling , and visible portion of occluded objects -LSB- 12 -RSB- -LSB- 9 -RSB- -LSB- 17 -RSB- -LSB- 3 -RSB- -LSB- 15 -RSB- . In addition , many appearance-based and model-based object recognition methods assumed that they have known occluded regions of objects or images through extensive training processes with statistical approach . However , our approach is not limited to recognizing occluded objects by pose and scale changes , and does not need extensive training processes . Unlike existing methods , our method finds shapes and regions to reconstruct occluded shapes and regions within objects . Our approach can handle object rotation and scaling for dealing with occlusion , and does not require extensive training processes . The main advantage of our approach is that it becomes simple to reconstruct objects from occlusions . We present the robust method , which is based on the contours of objects , for recognizing partially occluded objects based on symmetry properties . The contour-based approach finds a symmetry axis using the maximum diameter from the occluded object . In experiments , we demonstrate how our method reconstruct and recognize occluded shapes and regions using symmetry . Experiments use rotated and scaled objects for dealing with occlusion . We also evaluate the recognition rate of the reconstructed objects using symmetry and the visible portion of the occluded objects for recognition . The rest of this paper is organized as follows . In Section 2 , we briefly review work related to this study . In Section 3 , we describe a method to recognize partial objects from given classes . In Section 4 , we describe experimental results for partial object recognition . Finally , we summarize this paper in Section 5 . There have been several research efforts in object recognition for dealing with occlusion . Krumm -LSB- 13 -RSB- proposed a new algorithm for detecting objects in images which uses models based on training images of the object , with each model representing one pose . Williams -LSB- 23 -RSB- proposed a method for the reconstruction of solid-shape from image contour using the Huffman labeling scheme . For object recognition , Chang and Krumm -LSB- 3 -RSB- used the color cooccurrence histogram based on pairs of pixels . Schiele et al. -LSB- 20 -RSB- proposed a method to perform partial object recognition using statistical methods , which are based on multidimensional receptive field histograms . In addition , Rajpal et al. -LSB- 17 -RSB- introduced a method for partial object recognition using neural network based indexing . In appearance-based object recognition , Edwards and Murase -LSB- 6 -RSB- addressed the occlusion problem inherent in appearance-based methods using a mask to block out part of the basic eigenimages and the input image . Leonardis and Bischof -LSB- 14 -RSB- handled occlusion , scaling , and translation by randomly selecting image points from the scene and their corresponding points in the basis eigenvectors . Rao -LSB- 18 -RSB- applied the adaptive learning of eigenspace basis vectors in appearance-based methods . Ohba and Ikeuchi -LSB- 16 -RSB- were able to handle translation and occlusion of an object using eigenwindows . Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page . To copy otherwise , or republish , to post on servers or to redistribute to lists , requires prior specific permission and/or a fee . SAC ' 05 , March 13-17 , 2005 , Santa Fe , New Mexico , USA . Copyright 2005 ACM 1-58113-964-0 / 05/0003 ... $ 5.00 . 1190 2005 ACM Symposium on Applied Computing Current methods for dealing with occlusion have been based on template matching , statistical approaches using localized invariants , and recognition of occluded regions based on local features . In addition , there are many efforts in ellipse construction and detection -LSB- 7 -RSB- -LSB- 9 -RSB- -LSB- 22 -RSB- . In this paper , we propose unique methodologies in object recognition for dealing with occlusion based on symmetry properties through the ellipse reconstruction . Even though there have been several efforts in object recognition with occlusion , current methods have been highly sensitive to object pose and scaling . In addition , many appearance-based and model-based object recognition methods assumed that they have known occluded regions of objects or images through extensive training processes . However , our method is not limited to recognizing occluded objects by pose and scale changes , and do not require extensive training processes . We discuss the object reconstruction and the parameter estimation method to find the best matching class of input objects using the classification tree method -LSB- 4 -RSB- . We extracted shape parameters from reconstructed objects using RLC lines , such as roundness , aspect ratio , form factor , surface regularity -LSB- 5 -RSB- . The approach tries to find occluded shapes within partially occluded objects . The basic assumption is that most objects are represented by symmetrical figures . When a symmetric object is partially occluded , we use the symmetry measure to evaluate the symmetric shape . We estimate the most similar parameters of occluded shape and region of objects , and we retrieve objects that have the estimated parameters of occluded objects . A basic idea of reconstruction and estimation of occluded objects is to use symmetry properties within objects and use to the contour of objects . Fortunately , most products in electronic catalogs have symmetry in their shapes and they are represented by symmetrical figures . Symmetrical descriptions of shape or detection of symmetrical features of objects can be useful for shape matching , model-based object matching , and object recognition -LSB- 2 -RSB- -LSB- 1 -RSB- . In the given database , we have elliptical and roughly-rounded objects such as plates , cups , pans , and pots , depending on their poses and shapes . First , we consider elliptical objects in which the occlusion changes values of measurements and parameters related to diameters . We assume that we can get diameters from elliptical objects , which are partially occluded . Figure 3.1 Three-Spoke from the Triangle . However , the elliptical objects are limited to the shape of objects . Therefore , it may not be applied to other types of shape such as irregular shapes . In this case , since we can not easily detect the symmetry axes , we introduce the three-spoke type symmetry method as shown in Figure 3.1 . We apply this approach to roughly-rounded objects such as cups . For roughly-rounded objects , we use the three-spoke type method , which is derived from the triangle . The triangle is a basic model to represent figures such as circle , rectangle , and polygon . We use extended lines of the triangle to make axes as shown in Figure 3.1 . The three-spoke type symmetry axes , which are equally assigned by 120 degrees , provide the possibility to detect proper symmetry axes on roughly-rounded objects . Therefore , this method can detect symmetry axes in roughly-rounded objects . In order to perform the following procedures , we assume that objects are represented by symmetrical figures . 1 . We have an occluded elliptical object in Figure 3.2 and roughly-rounded object in Figure 3.6 , we can get cutting points of the occlusion -LRB- x , y -RRB- ' and -LRB- x , y -RRB- '' , that are given by overlapping or cutting . Figure 3.2 The Occlusion Area Estimation using Symmetry : Get cutting points -LRB- x , y -RRB- ' and -LRB- x , y -RRB- '' and get a distance l' . Figure 3.3 The Occlusion Area Estimation using Symmetry : Get the maximum diameter and the symmetry axis . Figure 3.4 The Occlusion Area Estimation using Symmetry : Get the estimated region a ' using a line l' and the symmetry axis . Figure 3.5 The Occlusion Area Estimation using Symmetry : Add region a ' to occluded shape and region and re-captured the estimated shape of an object . 2 . Compute a distance between two cutting points from -LRB- x , y -RRB- ' and -LRB- x , y -RRB- '' , which is called a line l' as in Figure 3.2 and 3.6 . 3 . Based on a line l' , make a connection between two points , fill the concave region and re-captured the shape . It is important to compute a centroid in an object . 4 . Get the maximum diameter from re-captured shape using extremal points as shown in Figure 3.4 and 3.7 . Two extremal points -LRB- r , l -RRB- and -LRB- r , l -RRB- ' from re-captured shape as in Figure 3.7 . The distance between two extreme boundary points are represented by the maximum diameter . 5 . In elliptical objects , one of the maximum and minimum diameters can be a symmetry axis . In roughly-rounded objects , we use the three-spoke type symmetry , one spoke can be a 1191 symmetry axis to find occluded region within an object . 6 . Centroid Detection : In case of elliptical objects , we find a centroid based on the maximum diameter and a line perpendicular to the maximum diameter , which is located in the center of the length of the maximum diameter . We select symmetry axes based on one of these lines as in Figure 3.3 . In roughly-rounded objects , we get a centroid , based on whole region of an object . Equation 2 is adapted from Russ -LSB- 19 -RSB- . If the centroid is calculated by equation 1 using the boundary pixels only , the results may not be correct . The calculated points will be biased toward whichever part of the boundary is most complex and contains the most pixels . The correct centroid location uses the pairs of coordinates i x , i y for each point in the shape boundary . The centroid of an irregular shape is calculated correctly using all of the pixels in an object . Area y C Area x C k i i y k i i x = = = = 0 0 , -LRB- 1 -RRB- Area x x y y C Area y y x x C k i i i i i y k i i i i i x + = + = 0 2 1 2 1 0 2 1 2 1 -RRB- -LRB- -RRB- -LRB- , -RRB- -LRB- -RRB- -LRB- -LRB- 2 -RRB- 7 . In roughly-rounded objects , a centroid is put at the same position at the center of the three-spoke type symmetry axes . Figure 3.6 The occlusion of a cup : Get a centroid after re-captured a shape . Figure 3.7 Get extremal points -LRB- r , l -RRB- , -LRB- r , l -RRB- ' and -LRB- r , l -RRB- '' , -LRB- r , l -RRB- '' ' and the maximum diameter of an object . Figure 3.8 Use the three spoke type symmetry : Match a center of the spoke to a centroid and parallel one of axes to the maximum diameter . Figure 3.9 Extend axes and make symmetry axes . Figure 3.10 Select a symmetry axis based on two regions , which are A and B. Figure 3.11 Find a region a ' of occluded shape using a symmetry axis and add to a occluded shape . 8 . Axis Detection : The midpoint of the major axis is called the center of the ellipse . The minor axis is the line segment perpendicular to the major axis which also goes through the center and touches the ellipse at two points . In elliptical objects , we detect a symmetry axis based on the maximum diameter or the minimum diameter . To find a symmetry axis in roughly-rounded objects , one of axes of the three-spoke type symmetry axes is in parallel with the maximum diameter of an object as shown in Figure 3.8 . Based on occluded shape and region , we select a symmetry axis to estimate this region within an object . Figures 3.9 and 3.10 show how to select a symmetry axis . When we select an axis in roughly-rounded objects , we consider conditions as follows : Select axes , which do n't intersect the occluded region . 3.9 and 3.10 show how to select a symmetry axis . Select axes , which have a region with the maximum diameter l' . Area and perimeter are invariants as in equation 3 , compare the proportion of region A and B. B A Area Perimeter Area Perimeter -LRB- 3 -RRB- 9 . Using mirror symmetry , we can get points across an axis . We find points on the contour across an axis which have the same length l' and the same angle corresponding to the axis that is perpendicular to a symmetry axis , but the distance between axis and points may or may not be the same . 10 . Capture a region a ' , move the captured region to the occluded shape using the mirror symmetry , and add to these regions as shown in Figure 3.4 , 3.5 , and 3.11 . 11 . Re-compute shape measurements such as area , diameters , and perimeter using RLC lines from re-captured shape of an object . Then , re-compute shape parameters based on measurements . 12 . Apply to a classifier . From the above discussions , we described how to reconstruct and estimate the partially occluded shape and region of an object and how to find the best matching class of partially occluded objects after the estimation . 1192 In the sections , we evaluate and describe the results of partial object recognition by our proposed a method . We have selected 190 partially occluded objects of images from electronic catalogs on the Internet as well as manipulated images . We assume that occluded objects have more than 50 % visibility of objects , and images of catalogs contain partially occluded objects . The objects are categorized by semantic meanings such as cup and plate . In addition , our approaches and experiments are limited to cups and plates since we use roughly-rounded or elliptical objects . More precisely , the database contains 32 objects from different viewpoints and images of 97 objects comprising image plane rotations and scale changes . In sample images , we have extracted image features of partially occluded objects such as shape and texture . We experimented with shape reconstruction based on the contour of objects using symmetry properties . We assumed that inputs are not correctly classified and have occlusion . We experimented with samples such as plates and cups to reconstruct the occluded shape of objects as shown in Figure 4.1 and 4.2 . In Figure 4.2 , it is correctly classified after the reconstruction with an occlusion about 30 % . On the other hand , Figure 4.1 is not correctly classified after the reconstruction since the width of plate is too narrow . This experiment shows that our method heavily relies on shape of objects . Figure 4.1 Example of the occlusion with a Plate . Figure 4.2 Example of the manipulated occlusion with a Cup . Finally , we performed an experiment for the relationships between visible portion of objects and recognition rates . In order to evaluate the visibility of objects , we used manipulated images of cups and plates . Figure 4.3 shows the pattern of object recognition in the presence of partial occlusion of objects and the results obtained by the symmetric recognition . A visible portion of approximately 67 % is sufficient for the recognition of objects based on the contour . Figure 4.3 Object recognition in the presence of the occlusion of objects based on the contour . There are many efforts in object recognition for dealing with occlusion . The visible portion of objects required to recognize occluded objects are shown in Table 4.1 . Table 4.1 shows a simple comparison between our method and other existing methods . The probabilistic method based on local measurements requires small portions of objects to recognize the whole objects , but it required extensive training processes to recognize occluded objects -LSB- 21 -RSB- -LSB- 20 -RSB- . Our method shows good visibility of partial object recognition and do not need extensive training processes . Table 4.1 The visibility of object recognition in the presence of partial occlusion . Methods Visibility Training processes Appearance matching techniques using adaptive masks 90 % not required Probabilistic technique using Chi-square 72 % required Probabilistic technique using local measurements 34 % required Contour-based approach using symmetry 67 % not required In order to measure the influence of occlusion and compare its impact on the recognition performance of the different methods , we performed an experiment as follows . Figure 4.4 summarizes the recognition results for different visible object portions . For each test object , we varied the visible object portion from 20 % to 100 % and recorded the recognition results using Chi-square divergence and our method . Figure 4.4 Experimental results with occlusion . 1193 The results show that our method clearly obtains better results than Chi-square divergence . Using only 60 % of the object area , almost 80 % of the objects are still recognized . This confirms that our method is capable of reliable recognition in the presence of occlusion . Table 4.2 Summary of Object Recognition Methods for dealing with Occlusion . Methods Occlusion Scale changes Object Pose Rotation Bischof et al. -LSB- 1 -RSB- Yes Yes No No Edwards et al. -LSB- 6 -RSB- Yes Yes No Yes -LRB- limited -RRB- Ohba et al. -LSB- 16 -RSB- Yes No Yes No Rao -LSB- 18 -RSB- Yes No Yes No Jacob et al. -LSB- 11 -RSB- Yes No Yes No Krumm -LSB- 13 -RSB- Yes No No NO Contour-based using symmetry Yes Yes Yes -LRB- limited -RRB- Yes Table 4.2 summarizes the various object recognition methods . The table indicates whether the methods can handle occlusion , rotation , pose , and changes in the size of objects in the database . Unlike the other methods , our method can handle scale change , object pose , and rotated objects with occlusion , even though our method has minor limitations of object poses . In this paper , we have discussed how to estimate parameters and to reconstruct the occluded shape of partial objects in image databases . In order to reconstruct occluded shapes , we used symmetry , which provides powerful method for the partial object recognition . Unlike the existing methods , our method tried to reconstruct occluded shapes and regions within objects , since most objects in our domain have symmetrical figures . However , we have limitations in the shape of objects and the occluded region of objects . For example , if a pan has an occlusion in handle , it can not correctly reconstruct and be recognized . Another minor limitation of our method is that a method is sensitive to the pose of an object . For example , if we can not see an ellipse due to the object 's pose , we can not recognize the object . After estimation , we have applied inputs , which include estimated parameters , to the existing classification trees , to get to the best matching class . All experiments are performed based on the classifier in earlier work . In experiments , the results show that the recognition of the occluded object is properly reconstructed , estimated , and classified , even though we have limited to the size of samples . In addition , we have experienced the power of the symmetry through experiments . -LSB- 1 -RSB- H. Bischof and A. Leonardis . Robust recognition of scaled eigenimages through a hierachical approach . In IEEE Conference on Computer Vision and Pattern Recognition , 1998 . -LSB- 2 -RSB- H. Blum and R.N. Nagel . Shape description using weighted symmetric axis features . Pattern Recognition , 1978 . -LSB- 3 -RSB- P. Chang and J. Krumm . Object Recognition with Color Cooccurrence Histograms . In IEEE Conference on Computer Vision and Pattern Recognition , 1999 . -LSB- 4 -RSB- J. Cho and N. Adam . Efficient Splitting Rules based on the Probabilities of Pre-Assigned Intervals . In IEEE Conference on Data Mining , 2001 . -LSB- 5 -RSB- J. Cho , A. Gangopadhyay and N. Adam . Feature Extraction for Content-based Image search in Electronic Commerce . In MIS/OA International Conference , 2000 . -LSB- 6 -RSB- J. Edwards and H. Murase . Appearance matching of occluded objects using coarse-to-fine adaptive masks . In IEEE Conference on Computer Vision and Pattern Recognition , 1997 . -LSB- 7 -RSB- A. W. Fitzgibbon , M. Pilu , and R. B. Fisher . Direct least squares fitting of ellipses . In International Conference on Pattern Recognition , 1996 . -LSB- 8 -RSB- M. Fleck . Local Rotational Symmetries . In IEEE Conference on Computer Vision and Pattern Recognition , 1986 . -LSB- 9 -RSB- C. Ho and L. Chan . A fast ellipse/circle detector using geometric symmetry . Pattern Recognition , 1995 . -LSB- 10 -RSB- Joachim Hornegger , Heinrich Niemann , and Robert Risack . Appearance-based object recognition using optimal feature transforms . Pattern Recognition , 2000 . -LSB- 11 -RSB- David W. Jacobs and Ronen Basri . 3D to 2D recognition with regions . In IEEE Conference on Computer Vision and Pattern Recognition , 1997 . -LSB- 12 -RSB- Grinnell Jones and Bir Bhanu . Recognition of articulated and occluded objects . IEEE Transaction on Pattern Analysis and Machine Intelligence , 1999 . -LSB- 13 -RSB- John Krumm . Object detection with vector quantized binary features . In IEEE Conference on Computer Vision and Pattern Recognition , 1997 . -LSB- 14 -RSB- Ales Leonardis and Horst Bishof . Dealing with Occlusions in the Eigenspace Approach . In IEEE Conference on Computer Vision and Pattern Recognition , 1996 . -LSB- 15 -RSB- David G. Lowe . Object Recognition from Local Scale-Invariant Features . In International Conference on Computer Vision , 1999 . -LSB- 16 -RSB- K. Ohba and K. Ikeuchi . Detectability , uniqueness , and reliability of eigen windows for stable verification of partially occluded objects . IEEE Trans . Pattern Anal . Mach , 1997 . -LSB- 17 -RSB- N. Rajpal , S. Chaudhury , and S. Banerjee . Recognition of partially occluded objects using neural network based indexing . Pattern Recognition , 1999 . -LSB- 18 -RSB- R. Rao . Dynamic appearance-based recognition . In IEEE Conference on Computer Vision and Pattern Recognition , 1997 . -LSB- 19 -RSB- John C. Russ . The Image Processing Handbook . CRC Press , 3rd edition , 1998 . -LSB- 20 -RSB- Bernt Schiele and Alex Pentland . Probabilistic Object Recognition and Localization . In International Conference on Computer Vision , 1999 . -LSB- 21 -RSB- H. Schneiderman and T. Kanade . Probabilistic modeling of local appearance and spatial relationships for object recognition . In IEEE Conference on Computer Vision and Pattern Recognition , 1998 -LSB- 22 -RSB- W. Wu and M. J. Wang . Elliptical object detection by using its geometrical properties . Pattern Recognition 1993 . -LSB- 23 -RSB- Lance R. Williams . Topological reconstruction of a smooth manifold-solid from its occluding contour . Journal of Computer Vision , 1997 . 1194

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image
symmetry
object recognition
occlusion
recognition
reconstruction
estimation
object
contour