Using

Probabilistic Modelsfor

Multimedia Retrieval

Arjen P. de Vriesarjen@acm.org

(Joint research with Thijs Westerveld)

Centrum voor Wiskunde en Informatica

E-BioSci/ORIEL Annual Workshop, Sep 3-5, 2003

• Eiffel tower

• scary/spooky Eiffel tower

•

Introduction

Outline

• Generative Models– Generative Model– Probabilistic retrieval– Language models, GMMs

• Experiments– Corel experiments– TREC Video benchmark

• Conclusions

What is a Generative Model?

• A statistical model for generating data– Probability distribution over samples in a

given ‘language’M

P ( | M ) = P ( | M )

P ( | M, )

P ( | M, )

P ( | M, )

© Victor Lavrenko, Aug. 2002

Generative Models

video of Bayesian model to that present the disclosure can a on for retrieval in have is probabilistic still of for of using this In that is to only queries queries visual combines visual information look search video the retrieval based search. Both get decision (a visual generic results (a difficult We visual we still needs, search. talk what that to do this for with retrieval still specific retrieval information a as model still

LMabstract

Unigram and higher-order models

• Unigram Models

• N-gram Models

• Other Models– Grammar-based models, etc.– Mixture models

= P ( ) P ( | ) P ( | ) P ( | )

P ( ) P ( ) P ( ) P ( )

P ( )

P ( ) P ( | ) P ( | ) P ( | )

© Victor Lavrenko, Aug. 2002

The fundamental problem• Usually we don’t know the model M

– But have a sample representative of that model

• First estimate a model from a sample

• Then compute the observation probability

P ( | M ( ) )

M© Victor Lavrenko, Aug. 2002

Indexing: determine models

•Indexing–Estimate

Gaussian Mixture Models from images using EM

–Based on feature vector with colour, texture and position information from pixel blocks

–Fixed number of components

Docs Models

Retrieval: use query likelihood

• Query:

• Which of the models is most likely to generate these 24 samples?

Probabilistic Image Retrieval

?

Query

Rank by P(Q|M)

P(Q|M1)

P(Q|M4)

P(Q|M3)

P(Q|M2)

Probabilistic Retrieval Model• Text

– Rank using probability of drawing query terms from document models

• Images– Rank using probability of drawing query blocks

from document models

• Multi-modal– Rank using joint probability of drawing query

samples from document models

• Unigram Language Models (LM)– Urn metaphor

Text Models

• P( ) ~ P ( ) P ( ) P ( ) P ( )

= 4/9 * 2/9 * 4/9 * 3/9

© Victor Lavrenko, Aug. 2002

Generative Models and IR

• Rank models (documents) by probability of generating the query

• Q:

• P( | ) = 4/9 * 2/9 * 4/9 * 3/9 = 96/9

• P( | ) = 3/9 * 3/9 * 3/9 * 3/9 = 81/9

• P( | ) = 2/9 * 3/9 * 2/9 * 4/9 = 48/9

• P( | ) = 2/9 * 5/9 * 2/9 * 2/9 = 40/9

The Zero-frequency Problem

• Suppose some event not in our example– Model will assign zero probability to that event– And to any set of events involving the unseen

event

• Happens frequently with language • It is incorrect to infer zero probabilities

– Especially when dealing with incomplete samples

?

Smoothing

• Idea: shift part of probability mass to unseen events

• Interpolation with background (General English)– Reflects expected frequency of events– Plays role of IDF

+(1-)

Image Models

• Urn metaphor not useful– Drawing pixels useless

• Pixels carry no semantics

– Drawing pixel blocks not effective • chances of drawing exact query blocks from document slim

• Use Gaussian Mixture Models (GMM)– Fixed number of Gaussian

components/clusters/concepts

?

Image Models

• Expectation-Maximisation (EM) algorithm– iteratively

• estimate component assignments• re-estimate component parameters

Component 1 Component 2 Component 3

ExpectationMaximization

E

M

ExpectationMaximization

animation

Component 1 Component 2 Component 3

E

M

Key-frame representation

Query model

split colour channels

Take samples

CrCbY

DCT coefficients position

EM algorithm

                                             675 9 12 11 1 9 4 1517 -9 -3 0 0 0 1 850 15 4 0 1 4 -2 1 1661 7 13 5 -5 11 3 1536 2 -4 0 1 1 0 844 5 4 -2 0 1 -2 1 2668 -7 13 3 -3 0 -1 1534 0 -5 0 0 0 0 837 3 3 -3 0 -2 1 1 3665 10 11 2 4 5 2 1534 0 -5 0 0 0 0 829 0 3 -1 0 0 0 1 4669 -5 18 7 -3 1 -5 1534 0 -5 0 0 0 0 833 -5 4 -1 0 3 -1 1 5

             

                                

Scary Formulas

Probabilistic Retrieval Model• Find document(s) D* with highest probability

given query Q (MAP):

• Equal Priors ML

• Approximated by minimum Kullback-Leibler divergence

)(

)()|(argmax)|(argmax*

QP

DPDQPQDPD ii

iii

)|(argmax*ii DQPD

dxDxPDxP

xPxPD

iqi

iqi

)|(log)|(argmax

)](||)([KLargmin*

• Query– Bag of textual terms– Bag of visual blocks

• Query model – empirical query

distribution

• KL distance

N

jijiq DxP

NdxDxPDxP

1

)|(log1

)|(log)|(

otherwise,0

,1

)|( QxNDxP q

},...,,{ 21 NxxxQ

Query Models

Corel Experiments

Testing the Model on Corel

• 39 classes, ~100 images each• Build models from all images• Use each image as query

– Rank full collection– Compute MAP (mean average precision)

• AP=average of precision values after each relevant image is retrieved

• MAP is mean of AP over multiple queries

– Relevant from query class

Example resultsQuery:

Top 5:

MAP per Class (mean: .12)

• English Pub Signs .36• English Country Gardens .33• Arabian Horses .31• Dawn & Dusk .21• Tropical Plants .19• Land of the Pyramids .19• Canadian Rockies .18• Lost Tribes .17• Elephants .17• Tigers .16• Tropical Sea Life .16• Exotic Tropical Flowers .16• Lions .15• Indigenous People .15• Nesting Birds .13• …

• …• Sweden .07• Ireland .07• Wildlife of the Galapagos .07• Hawaii .07• Rural France .07• Zimbabwe .07• Images of Death Valley .07• Nepal .07• Foxes & Coyotes .06• North American Deer .06• California Coasts .06• North American Wildlife .06• Peru .05• Alaskan Wildlife .05• Namibia .05

Class confusion

• Query from class A

• Relevant from class B

• Queries retrieve images from own class

• Interesting mix-ups– Beaches – Greek islands

– Indigenous people – Lost tribes

– English country gardens – Tropical plants – Arabian Horses

• Similar backgrounds

Tuning the Models

• Yet another subset of Corel data– 39 classes, 10 images each– Index as before and calculate MAP

• Vary model parameters– NY: Number of DCT coefficients from Y channel

(1,3,6,10,15,21)

– NCbCr: Number of DCT coefficients from CB and Cr channels (0,1,NY)

– Xypos: Do/do not use position of samples

– C: number of components in GMM (1,2,4,8,16,32)

Example Image

Example models + samples Varying C, NY=10, NCbCr=1, Xypos=1

C=4 C=8 C=32

Example models + samples Varying NCbCr, NY=10, Xypos=1, C=8

NCbCr=0 NCbCr=1 NCbCr=10

MAP with different parameters

NCbCr Xypos C=1 C=2 C=4 C=8 C=16 C=32

0 0 .08 .18 .20 .21 .21 .21

0 1 .09 .19 .21 .21 .21 .20

1 0 .13 .22 .23 .23 .23 .23

1 1 .13 .22 .23 .23 .23 .22

10 0 .12 .22 .24 .24 .24 .23

10 1 .13 .21 .24 .24 .24 .23

Statistical Significance

• Mixture better than single Gauss (c>1)• Small differences between settings

– Yet, small differences might be significant• Wilcoxon signed-rank test (sign. level 5%)

A B Diff Rank Signrnk

97 96 -1 1 -1

88 86 -2 2.5 -2.5

75 79 4 4 4

90 88 -2 2.5 -2.5

85 93 8 5 5

m=87 m=88.4 =15 = Z+,Z-

Z+=9 Z-=6

=7.5

Statistical Significance

• Results– Optimal number of components at C=8

• Fewer components -> insufficient resolution• More components -> overfitting

– Colour information is important (NCbCr >0)• More is better if enough components

– Position information undecided • although using it never harms

Background MatchingQuery:

Top 5:

Background MatchingQuery:

Top 5:

TREC Experiments

TREC Video Track

• Goal: Promote progress in content-based video retrieval via metric based evaluation

• 25 Topics– Multimedia descriptions of an information need; 22

had video examples (avg. 2.7 each), 8 had image (avg. 1.9 each)

• Task is to return up to 100 best shots– NIST assessors judged top 50 shots from each

submitted result set; subsequent full judgements showed only minor variations in performance

Video Data

• Used mainly Internet Archive– advertising, educational, industrial, amateur films

1930-1970 – Noisy, strange color, but real archive data– 73.3 hours, partitioned as follows:

4.85

5.07

23.26

40.12Search test

Feature development(training and validation)

Feature test

Shot boundary test

Video Representation

• Video as sequence of shots (all TREC)

– Common ground truth shot set used in evaluation; 14,524 shots

• Shot = image + text (CWI specific):– Key-frame (middle frame of shot)– ASR Speech Transcript (LIMSI)

Search Topics

• Requesting shots with specific or generic:– People, Things, Locations, Activities

George Washington Football players

Search Topics

• Requesting shots with specific or generic:– People, Things, Locations, Activities

Golden Gate Bridge Sailboats

Search Topics

• Requesting shots with specific or generic:– People, Things, Locations, Activities

Overhead views of cities

Search Topics

• Requesting shots with specific or generic:– People, Things, Locations, Activities

Rocket taking off

Search Topics Summary

• Requested shots with specific/generic:– Combinations of the above:

• People spending leisure time at the beach• Locomotive approaching the viewer• Microscopic views of living cells

Experiments

• …with official TREC measures – Query representation– Textual/Visual/Combined runs

• …without measures; inspecting visual similarity– Selecting components– Colour vs. texture– EM initialisation

Measures• Precision

– fraction of retrieved documents that is relevant

• Recall – fraction of relevant documents that is retrieved

• Average Precision– precision averaged over different levels of recall

• Mean Average Precision (MAP) – mean of average precision over all queries

Textual and Visual runs

• Textual– Short Queries (Topic description)

– Long Queries (Topic description + transcripts from video examples)

• Visual– All examples– Best examples

• Combined– Simply add textual and visual log-likelihood scores

(joint probability of seeing both query terms and query blocks)

Textual and Visual runs

• Textual > Visual• Tlong > Tshort

• Combining overall not useful

• If both visual and textual runs good, combining improves

RUN MAP

Tshort 0.0916

Tlong 0.1212

BoBfull 0.0287

BoBbest 0.0444

BoBbest+Tshort 0.0784

BoBbest + Tlong 0.0870

Visual runs

• Scores for purely visual runs low (MAP .037)

• Drop further when video examples are removed from relevance judgements

Observation

• CBR successful under two conditions:– the query example is derived from the

same source as the target objects – a domain-specific detector is at hand

vt076: Find shots with James H. Chandler

Top 10:

Retrieval Results

• Non-interactive results disappointing– MAP across all participants/systems .056– Ignoring ASR runs, MAP drops to .044

• Only Known-item retrieval possible– MAP for queries with examples from

collection .094– MAP without these .026 (-40% from average)

• No significant differences between variants

Selecting Query Images

• Find shots of the Golden Gate Bridge

• Full topic – use all examples

• Best example – compute results for individual examples and find best

• Manual example– manually select good example from ones available in topic

Selecting Query Images• In general Best > Full (MAP full: 0.0287, best: 0.444)

• Sometimes Full > Best

Selecting Components

• Query articulation can improve retrieval effectiveness, but requires enormous user effort [lowlands2001]

• Document models (GMM), allow for easy selection of important regions [LL10]

Selecting Components

• For each topic we manually selected meaningful components

• No improvement in MAP

• Perhaps useful for more general queries (feature detection?)– Further investigation necessary

Component Search

Component Search

1-3:

18:

Being lucky…

1-3:

10 17 68

Rel.:

Visually similar by chanceVisual NOT similarKeyframe does not represent shot

Informal Results Analysis

• Forget about MAP scores• Investigate two aspects of experimental

results– How is image similarity captured

• Look at top 10 results

– How do visual results contribute to (MAP) scores

• Look at key-frames from relevant shots in top 100

• Qualitative observations

Some Observations

• Colour dominates texture

• Homogeneous Queries – Semantically similar results– …or at least visually similar

• Heterogeneous queries– Results dominated by subset of query

Some Observations

• Colour dominates texture

Colour dominates texture

Some Observations

• Colour dominates texture

• Homogeneous queries give intuitive results– Semantically similar– ... or at least visually

Homogeneous querywith semantics

Homogeneous queryno semantics, but visual similarity

Top 5 audience

Top 5 grass:

Full query Audience component Grass component

Some Observations

• Colour dominates texture

• Homogeneous queries give intuitive results– Semantically similar– ... or at least visually

• Results for heterogeneous queries often dominated by part of samples

Heterogeneous queryfull query

M M M M M

Heterogenous querygrass samples

M M M M M

Heterogeneous query

• Possible explanations domination sky samples– no document in the collection explains grass

samples well– sky samples well explained by any document

(i.e. background probability is high)

• Smoothing with background probabilities might help

Heterogenous querieswith smoothing

M M M M M

•Smoothing seems to help somewhat, but problem not solved•Looking for model which favors documents with balanced individual sample scores

Controlled Experiments

• What determines visual similarity in the generative probabilistic model

• Small special purpose collections created from the large TREC video collection

1. Emphasis on colour information

2. Role of initialisation of the mixture models

Colour Experiments

• Collection with 2 copies of each frame– Original colour image

– Greyscale version

• Build models– Models can describe colour and texture

• Search using colour and greyscale queries

Colour Experiments

M1A M1B M2BM2A MNBMNA

P( | )~ P( | )MiAMiB

P( | )~ P( | )MiAMiB

Distance between pairsmodels without colour

• Results

P( | )

Ranks 2.9

P( | )• Results

Ranks 2.0

Distance between pairsmodels with colour

• Results

Ranks 89.7

P( | )

Ranks 7.3

P( | )• Results

Indeed colour dominates texture

Colour Experiments

• Conclusion:– Model from colour image only captures colour information

Queries Modelsrank 1

rank 1

rank 7.3

rank 89.7

EM initialisation

• EM sensitive to initialisation– Build collection with several models for

each frame– Compare scores for different models from

same frame– Concentrate on top ranks

EM initialisation

• Collection with:– 2 Videos

– 5 frames / shot

– 10 models / frame• From random initialisations

• Models from same frame should have similar scores

EM initialisation

Ranks

Set Mean Std-dev

Frame 8.06 5.95

Shot 269.85 35.09

Video 2946.10 286.15

Collection 3075.5 374.02 cc

EM initialisation

• Results– Models from query frame all near top list

• Mean rank: 8.06, std.dev.5.95

– Models from same shot closer together than models from other frames

– In general: higher ranking frames have their models closer together

• Although EM sensitive to initialisation, this does not affect ranking much

Concluding Remarks

Lessons TREC-10• Generalization remains a problem

– Good results examples from collection

• Textual search outperforms visual search– Even with topics designed for visual retrieval!

• Successful visual retrieval often traces down to involving luck (background, known-item)

• Combining textual and visual results possible in the presented framework– When both have reasonable performance,

combination outperforms individual runs

Lessons TREC-10• Components queries retrieve intuitive results

• Convenient for query articulation!

• Color dominates texture• Sensitivity EM to initialization does not harm

results

• Note:Findings specific for model, but at least suggest hypotheses for others to investigate

Need 4 Test Collections

• Results on one collection do not automatically transfer to another– Multiple collections needed to conclude one

technique is better than other

• What is a good Test Collection?– Should be representative of realistic task

• This is what TREC tries to achieve

– Results should be measurable • Like when using Corel

Plans for TREC-11

• Better video representation– More frames per shot– Audio GMM (on MFCC)

• Spatial and temporal aspects– Shot = background + “objects”

• Special research interest in the right balance between interactive query articulation and (semi-)automatic query formulation

Future plans

• Balancing results for heterogeneous queries

• Propagating generic concepts

Care for more?

A probabilistic Multimedia Retrieval Model and Its Evaluation, Thijs Westerveld, Arjen de Vries, Alex van Ballegooij, Franciska de Jong and Djoerd Hiemstra, EURASIP journal on Applied Signal Processing 2003:2

arjen@acm.org