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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
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