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ImageCLEF - Experimental Evaluation in Visual Information Retrieval

of: Henning Müller, Paul Clough, Thomas Deselaers, Barbara Caputo

Springer-Verlag, 2010

ISBN: 9783642151811 , 544 Pages

Format: PDF, Read online

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ImageCLEF - Experimental Evaluation in Visual Information Retrieval


 

Foreword

6

Preface

8

Acknowledgements

10

Contents

12

List of Contributors

24

Introduction

28

Seven Years of Image Retrieval Evaluation

30

Introduction

30

Evaluation of IR Systems

32

IR Test Collections

33

Cross--Language Evaluation Forum (CLEF)

36

ImageCLEF

36

Aim and Objectives

36

Tasks and Participants

38

Data sets

39

Contributions

39

Organisational Challenges

41

Conclusions

42

References

43

Data Sets Created in ImageCLEF

46

Introduction

46

Collection Creation

47

Requirements and Specification

48

Collection Overview

50

Image Collections for Photographic Retrieval

51

The St. Andrews Collection of Historic Photographs

51

The IAPR TC--12 Database

53

The Belga News Agency Photographic Collection

55

Image Collections for Medical Retrieval

56

The ImageCLEFmed Teaching Files

57

The RSNA Database

61

Automatic Image Annotation and Object Recognition

62

The IRMA Database

62

The LookThatUp (LTU) Data set

63

The PASCAL Object Recognition Database

64

The MIR Flickr Image Data Set

65

Image Collections in Other Tasks

65

The INEX MM Wikipedia Collection

66

The KTH--IDOL2 Database

67

Conclusions

68

References

69

Creating Realistic Topics for Image Retrieval Evaluation

71

Introduction

71

User Models and Information Sources

74

Machine--Oriented Evaluation

74

User Models

75

Information Sources for Topic Creation

76

Concrete Examples for Generated Visual Topics in Several Domains

79

Photographic Retrieval

79

Medical Retrieval

80

The Influence of Topics on the Results of Evaluation

81

Classifying Topics Into Categories

82

Links Between Topics and the Relevance Judgments

83

What Can Be Evaluated and What Can Not?

83

Conclusions

84

References

85

Relevance Judgments for Image Retrieval Evaluation

88

Introduction

88

Overview of Relevance Judgments in Information Retrieval

89

Test Collections

89

Relevance Judgments

90

Relevance Judging for the ImageCLEF Medical Retrieval Task

97

Topics and Collection

97

Judges

98

Relevance Judgment Systems and the Process of Judging

99

Conclusions and Future Work

103

References

104

Performance Measures Used in Image Information Retrieval

106

Evaluation Measures Used in ImageCLEF

106

Measures for Retrieval

107

Measuring at Fixed Recall

108

Measuring at Fixed Rank

110

Measures for Diversity

112

Collating Two Measures Into One

113

Miscellaneous Measures

113

Considering Multiple Measures

114

Measures for Image Annotation and Concept Detection

115

Use of Measures in ImageCLEF

116

Conclusions

117

References

117

Fusion Techniques for Combining Textual and Visual Information Retrieval

120

Introduction

120

Information Fusion and Orthogonality

122

Methods

123

Results

123

Early Fusion Approaches

123

Late Fusion Approaches

124

Inter--media Feedback with Query Expansion

129

Other Approaches

130

Overview of the Methods from 2004--2009

130

Justification for the Approaches and Generally Known Problems

130

Conclusions

133

References

133

Track Reports

140

Interactive Image Retrieval

142

Interactive Studies in Information Retrieval

142

iCLEF Experiments on Interactive Image Retrieval

144

iCLEF Image Retrieval Experiments: The Latin Square Phase

145

iCLEF Experiments with Flickr

148

The Target Collection: Flickr

149

Annotations

149

The Task

150

Experiments

152

Task Space, Technology and Research Questions

159

Use Cases for Interactive Image Retrieval

159

Challenges: Technology and Interaction

160

References

162

Photographic Image Retrieval

165

Introduction

165

Ad hoc Retrieval of Historic Photographs: ImageCLEF 2003--2005

166

Test Collection and Distribution

167

Query Topics

168

Relevance Judgments and Performance Measures

171

Results and Analysis

171

Ad hoc Retrieval of Generic Photographs: ImageCLEFphoto 2006-2007

173

Test Collection and Distribution

174

Query Topics

175

Relevance Judgments and Performance Measures

176

Results and Analysis

177

Visual Sub--task

178

Ad hoc Retrieval and Result Diversity: ImageCLEFphoto 2008--2009

179

Test Collection and Distribution

179

Query Topics

180

Relevance Judgments and Performance Measures

182

Results and Analysis

182

Conclusion and Future Prospects

184

References

185

The Wikipedia Image Retrieval Task

187

Introduction

187

Task Overview

188

Evaluation Objectives

188

Wikipedia Image Collection

189

Additional Resources

189

Topics

190

Relevance Assessments

191

Evaluation

193

Participants

193

Approaches

194

Results

199

Discussion

203

Best Practices

203

Open Issues

204

Conclusions and the Future of the Task

205

References

205

The Robot Vision Task

208

Introduction

208

The Robot Vision Task at ImageCLEF 2009: Objectives and Overview

210

The Robot Vision Task 2009

211

Robot Vision 2009: The Database

211

Robot Vision 2009: Performance Evaluation

212

Robot Vision 2009: Approaches and Results

215

Moving Forward: Robot Vision in 2010

217

The Robot Vision Task at ICPR2010

217

The Robot Vision Task at ImageCLEF2010

219

Conclusions

220

References

220

Object and Concept Recognition for Image Retrieval

222

Introduction

222

History of the ImageCLEF Object and Concept Recognition Tasks

223

2006: Object Annotation Task

224

2007: Object Retrieval Task

225

2008: Visual Concept Detection Task

226

2009: Visual Concept Detection Task

227

Approaches to Object Recognition

227

Descriptors

229

Feature Post--processing and Codebook Generation

230

Classifier

230

Post--Processing

231

Results

231

2006: Object Annotation Task

232

2007: Object Retrieval Task

232

2008: Visual Concept Detection Task

233

2009: Visual Concept Detection Task

234

Evolution of Concept Detection Performance

236

Discussion

237

Combinations with the Photo Retrieval Task

238

Conclusion

238

References

239

The Medical Image Classification Task

243

Introduction

243

History of ImageCLEF Medical Annotation

244

The Aim of the Challenge

244

The Database

245

Error Evaluation

249

Approaches to Medical Image Annotation

251

Image Representation

252

Classification Methods

252

Hierarchy

253

Unbalanced Class Distribution

253

Results

253

Conclusion

257

References

259

The Medical Image Retrieval Task

261

Introduction

261

Participation in the Medical Retrieval Task

262

Development of Databases and Tasks over the Years

264

2004

264

2005--2007

265

2008--2009

269

Evolution of Techniques Used by the Participants

271

Visual Retrieval

272

Textual Retrieval

272

Combining Visual and Textual Retrieval

273

Case--Based Retrieval Topics

273

Results

273

Visual Retrieval

274

Textual Retrieval

274

Mixed Retrieval

275

Relevance Feedback and Manual Query Reformulation

275

Main Lessons Learned

275

Conclusions

277

References

277

Participant reports

280

Expansion and Re--ranking Approaches for Multimodal Image Retrieval using Text--based Methods

282

Introduction

283

Integrated Retrieval Model

284

Handling Multi--modality in the Vector Space Model

285

Document and Query Expansion

286

Re--ranking

288

Level 1: Narrowing-down and Re-indexing

290

Level 2: Cover Coefficient Based Re--ranking

290

Results

292

Conclusions

294

References

295

Revisiting Sub--topic Retrieval in the ImageCLEF 2009 Photo Retrieval Task

297

Introduction

298

Background and Related Work

300

Sub--topic Retrieval

300

The Probability Ranking Principle

302

Beyond Independent Relevance

302

Document Clustering and Inter--Cluster Document Selection

304

Re--examining Document Clustering Techniques

304

Clustering for Sub--topic Retrieval

305

Empirical Study

307

Results

310

Conclusions

311

References

313

Knowledge Integration using Textual Information for Improving ImageCLEF Collections

315

Introduction

315

System Description

317

Photo Retrieval System

317

Medical Retrieval System

318

Photo Task

318

Using Several IR and a Voting System

321

Filtering

322

Clustering

325

The Medical Task

326

Metadata Selection using Information Gain

326

Expanding with Ontologies

328

Fusion of Visual and Textual Lists

331

Conclusion and Further Work

331

References

333

Leveraging Image, Text and Cross--media Similarities for Diversity--focused Multimedia Retrieval

334

Introduction

334

Content--Based Image Retrieval

336

Fisher Vector Representation of Images

337

Image Retrieval at ImageCLEF Photo

339

Text Representation and Retrieval

340

Language Models

340

Text Enrichment at ImageCLEF Photo

341

Text--Image Information Fusion

345

Cross--Media Similarities

346

Cross--Media Retrieval at ImageCLEF Photo

348

Diversity--focused Multimedia Retrieval

351

Re--ranking Top--Listed Documents to Promote Diversity

352

Diversity--focused Retrieval at ImageCLEF Photo

355

Conclusion

358

References

359

University of Amsterdam at the Visual Concept Detection and Annotation Tasks

362

Introduction

362

Concept Detection Pipeline

363

Point Sampling Strategy

364

Color Descriptor Extraction

365

Bag--of--Words model

366

Machine Learning

367

Experiments

368

Spatial Pyramid Levels

368

Point Sampling Strategies and Color Descriptors

369

Combinations of Sampling Strategies and Descriptors

370

Discussion

372

ImageCLEF 2009

372

Evaluation Per Image

374

Conclusion

374

ImageCLEF@ICPR 2010

375

Conclusion

375

References

376

Intermedia Conceptual Indexing

378

Introduction

378

Conceptual Indexing

380

Concept Usage and Definition in IR

380

Concept Mapping to Text

381

Mapping Steps

382

IR Models Using Concepts

385

Experiments using the ImageCLEF Collection

386

Image Indexing using a Visual Ontology

388

Image Indexing Based on VisMed Terms

389

FlexiTile Matching

392

Medical Image Retrieval Using VisMed Terms

393

Spatial Visual Queries

394

Multimedia and Intermedia Indexing

395

Conclusions

397

References

398

Conceptual Indexing Contribution to ImageCLEF Medical Retrieval Tasks

400

Introduction

401

Semantic Indexing Using Ontologies

401

Conceptual Indexing

402

Language Models for Concepts

402

Concept Detection

403

Concept Evaluation Using ImageCLEFmed 2005--07

404

From Concepts to Graphs

405

A Language Model for Graphs

405

Graph Detection

406

Graph Results on ImageCLEFmed 2005--07

407

Mixing Concept Sources

407

Query Fusion

408

Document Model Fusion

408

Joint Decomposition

409

Results on ImageCLEFmed 2005--07

411

Adding Pseudo--Feedback

412

Pseudo--Relevance Feedback Model

412

Results

413

Conclusions

414

References

414

Improving Early Precision in the ImageCLEF Medical Retrieval Task

416

Introduction

416

What is Early Precision?

417

Why Improve Early Precision?

418

ImageCLEF

418

Our System

419

User Interface

419

Image Database

420

Query Parsing and Indexing

421

Improving Precision

422

Modality Filtration

422

Using Modality Information for Retrieval

425

Using Interactive Retrieval

427

Conclusions

430

References

431

Lung Nodule Detection

433

Introduction

433

Lung Cancer --- Clinical Motivation

434

Computer--Aided Detection of Lung Nodules

436

Ground Truth for Lesions

437

Review of Existing Techniques

438

Gray--Level Threshold

439

Template Matching

439

Spherical Enhancing Filters

440

Description of Siemens LungCAD System

441

Lung Segmentation

441

Candidate Generation

441

Feature Extraction

442

Classification

443

Multiple Instance Learning

443

Exploiting Domain Knowledge in Data--Driven Training--Gated Classifiers

444

Ground Truth Creation: Learning from Multiple Experts

445

ImageCLEF Challenge

446

Materials and Methods

446

Results

447

Discussion and Conclusions

448

Clinical Impact

448

Future Extensions of CAD

450

References

451

Medical Image Classification at Tel Aviv and Bar Ilan Universities

453

Introduction

453

Visual Words in Medical Archives

454

The Proposed TAU--BIU Classification System Based on a Dictionary of Visual--Words

455

Patch Extraction

456

Feature Space Description

456

Quantization

457

From an Input Image to a Representative Histogram

458

Classification

459

Experiments and Results

460

Sensitivity Analysis

462

Optimizing the Classifier

464

Classification Results

467

Discussion

468

References

469

Idiap on Medical Image Classification

470

Introduction

470

Multiple Cues for Image Annotation

471

High--Level Integration

472

Mid--Level Integration

473

Low--Level Integration

473

Exploiting the Hierarchical Structure of Data: Confidence Based Opinion Fusion

474

Facing the Class Imbalance Problem: Virtual Examples

475

Experiments

475

Features

475

Classifier

478

Experimental Set--up and Results

479

Conclusions

480

References

481

External views

483

Press Association Images --- Image Retrieval Challenges

485

Press Association Images --- A Brief History

485

The Press Association

485

Images at the Press Association

487

User Search Behaviour

488

Types of Users

488

Types of Search

489

Challenges

490

Semantic Web for Multimedia Applications

491

Introduction to the Semantic Web

491

Success Stories and Research Areas

491

The Semantic Web Project at Press Association Images

493

Utilizing Semantic Web Technologies for Improving User Experience in Image Browsing

494

PA Data set: Linking to the Linked Data Cloud

494

Information Extraction and Semantic Annotation

496

Conclusions and Future Work

497

References

497

Image Retrieval in a Commercial Setting

499

Introduction

499

Evaluating Large Scale Image Search Systems

502

Query Logs and Click Data

503

Background Information on Image Search

506

Multilayer Perceptron

507

Click Data

509

Data Representation

511

Textual Features

511

Visual Features

513

Evaluation and Results

514

Analysis of Features

516

Discussion of Results

518

Looking Ahead

519

References

520

An Overview of Evaluation Campaigns in Multimedia Retrieval

522

Introduction

522

ImageCLEF in Multimedia IR (MIR)

524

INEX XML Multimedia Track

525

MIREX

526

GeoCLEF

526

TRECVid

527

VideOlympics

529

PASCAL Visual Object Classes (VOC) Challenge

529

MediaEval and VideoCLEF

530

Past Benchmarking Evaluation Campaigns

531

Comparison with ImageCLEF

532

Utility of Evaluation Conferences

533

Impact and Evolution of Metrics

534

Conclusions

536

References

537

Glossary

541

Index

546