Machine Learning Techniques for Multimedia
Case Studies on Organization and Retrieval
Bearbeitet von
Matthieu Cord, Pádraig Cunningham
1. Auflage 2008. Buch. xvi, 289 S. Hardcover ISBN 978 3 540 75170 0
Format (B x L): 15,5 x 23,5 cm Gewicht: 625 g
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Part I Introduction to Learning Principles for Multimedia Data
1 Introduction to Bayesian Methods and Decision Theory . . . . 3
Simon P. Wilson, Rozenn Dahyot, and P´adraig Cunningham 1.1 Introduction . . . 3
1.2 Uncertainty and Probability . . . 4
1.2.1 Quantifying Uncertainty . . . 4
1.2.2 The Laws of Probability . . . 5
1.2.3 Interpreting Probability . . . 6
1.2.4 The Partition Law and Bayes’ Law . . . 7
1.3 Probability Models, Parameters and Likelihoods . . . 8
1.4 Bayesian Statistical Learning . . . 9
1.5 Implementing Bayesian Statistical Learning Methods . . . 10
1.5.1 Direct Simulation Methods . . . 11
1.5.2 Markov Chain Monte Carlo . . . 12
1.5.3 Monte Carlo Integration . . . 13
1.5.4 Optimization Methods . . . 14
1.6 Decision Theory . . . 15
1.6.1 Utility and Choosing the Optimal Decision . . . 16
1.6.2 Where Is the Utility? . . . 17
1.7 Naive Bayes . . . 17
1.8 Further Reading . . . 18
References . . . 19
2 Supervised Learning. . . . 21
P´adraig Cunningham, Matthieu Cord, and Sarah Jane Delany 2.1 Introduction . . . 21
2.2 Introduction to Statistical Learning . . . 22
2.2.1 Risk Minimization . . . 22
2.2.2 Empirical Risk Minimization . . . 23
2.2.3 Risk Bounds . . . 24
ix
2.3 Support Vector Machines and Kernels . . . 26
2.3.1 Linear Classification: SVM Principle . . . 26
2.3.2 Soft Margin . . . 27
2.3.3 Kernel-Based Classification . . . 28
2.4 Nearest Neighbour Classification . . . 29
2.4.1 Similarity and Distance Metrics . . . 31
2.4.2 Other Distance Metrics for Multimedia Data . . . 32
2.4.3 Computational Complexity . . . 35
2.4.4 Instance Selection and Noise Reduction . . . 36
2.4.5 k-NN: Advantages and Disadvantages . . . 39
2.5 Ensemble Techniques . . . 40
2.5.1 Introduction . . . 40
2.5.2 Bias–Variance Analysis of Error . . . 41
2.5.3 Bagging . . . 41
2.5.4 Random Forests . . . 44
2.5.5 Boosting . . . 45
2.6 Summary . . . 46
References . . . 47
3 Unsupervised Learning and Clustering . . . . 51
Derek Greene, P´adraig Cunningham, and Rudolf Mayer 3.1 Introduction . . . 51
3.2 Basic Clustering Techniques . . . 52
3.2.1 k-Means Clustering . . . 52
3.2.2 Fuzzy Clustering . . . 53
3.2.3 Hierarchical Clustering . . . 54
3.3 Modern Clustering Techniques . . . 58
3.3.1 Kernel Clustering . . . 58
3.3.2 Spectral Clustering . . . 60
3.4 Self-organizing Maps . . . 65
3.4.1 SOM Architecture . . . 66
3.4.2 SOM Algorithm . . . 66
3.4.3 Self-organizing Map and Clustering . . . 69
3.4.4 Variations of the Self-organizing Map . . . 70
3.5 Cluster Validation . . . 73
3.5.1 Internal Validation . . . 75
3.5.2 External Validation . . . 79
3.5.3 Stability-Based Techniques . . . 84
3.6 Summary . . . 87
References . . . 87
4 Dimension Reduction . . . . 91
P´adraig Cunningham 4.1 Introduction . . . 91
4.2 Feature Transformation . . . 93
4.2.1 Principal Component Analysis . . . 94
4.2.2 Linear Discriminant Analysis . . . 97
4.3 Feature Selection . . . 99
4.3.1 Feature Selection in Supervised Learning . . . 99
4.3.2 Unsupervised Feature Selection . . . 104
4.4 Conclusions . . . 110
References . . . 110
Part II Multimedia Applications 5 Online Content-Based Image Retrieval Using Active Learning. . . . 115
Matthieu Cord and Philippe-Henri Gosselin 5.1 Introduction . . . 115
5.2 Database Representation: Features and Similarity . . . 117
5.2.1 Visual Features . . . 117
5.2.2 Signature Based on Visual Pattern Dictionary . . . 117
5.2.3 Similarity . . . 118
5.2.4 Kernel Framework . . . 119
5.2.5 Experiments . . . 120
5.3 Classification Framework for Image Collection . . . 121
5.3.1 Classification Methods for CBIR . . . 122
5.3.2 Query Updating Scheme . . . 123
5.3.3 Experiments . . . 123
5.4 Active Learning for CBIR . . . 124
5.4.1 Notations for Selective Sampling Optimization . . . 125
5.4.2 Active Learning Methods . . . 125
5.5 Further Insights on Active Learning for CBIR . . . 127
5.5.1 Active Boundary Correction . . . 128
5.5.2 MAP vs Classification Error . . . 130
5.5.3 Batch Selection . . . 130
5.5.4 Experiments . . . 132
5.6 CBIR Interface: Result Display and Interaction . . . 132
References . . . 136
6 Conservative Learning for Object Detectors . . . . 139
Peter M. Roth and Horst Bischof 6.1 Introduction . . . 140
6.2 Online Conservative Learning . . . 143
6.2.1 Motion Detection . . . 143
6.2.2 Reconstructive Model . . . 144
6.2.3 Online AdaBoost for Feature Selection . . . 146
6.2.4 Conservative Update Rules . . . 148
6.3 Experimental Results . . . 149
6.3.1 Description of Experiments . . . 149
6.3.2 CoffeeCam . . . 151
6.3.3 Switch to Caviar . . . 153
6.3.4 Further Detection Results . . . 156
6.4 Summary and Conclusions . . . 156
References . . . 156
7 Machine Learning Techniques for Face Analysis. . . . 159
Roberto Valenti, Nicu Sebe, Theo Gevers, and Ira Cohen 7.1 Introduction . . . 160
7.2 Background . . . 160
7.2.1 Face Detection . . . 160
7.2.2 Facial Feature Detection . . . 161
7.2.3 Emotion Recognition Research . . . 162
7.3 Learning Classifiers for Human–Computer Interaction . . . 163
7.3.1 Model Is Correct . . . 165
7.3.2 Model Is Incorrect . . . 166
7.3.3 Discussion . . . 167
7.4 Learning the Structure of Bayesian Network Classifiers . . . 168
7.4.1 Bayesian Networks . . . 168
7.4.2 Switching Between Simple Models . . . 169
7.4.3 Beyond Simple Models . . . 169
7.4.4 Classification-Driven Stochastic Structure Search . . . 170
7.4.5 Should Unlabeled Be Weighed Differently? . . . 171
7.4.6 Active Learning . . . 172
7.4.7 Summary . . . 173
7.5 Experiments . . . 173
7.5.1 Face Detection Experiments . . . 174
7.5.2 Facial Feature Detection . . . 178
7.5.3 Facial Expression Recognition Experiments . . . 183
7.6 Conclusion . . . 184
References . . . 185
8 Mental Search in Image Databases: Implicit Versus Explicit Content Query. . . . 189
Simon P. Wilson, Julien Fauqueur, and Nozha Boujemaa 8.1 Introduction . . . 189
8.2 “Mental Image Search” Versus Other Search Paradigms . . . 190
8.3 Implicit Content Query: Mental Image Search Using Bayesian Inference . . . 191
8.3.1 Bayesian Inference for CBIR . . . 191
8.3.2 Mental Image Category Search . . . 193
8.3.3 Evaluation . . . 195
8.3.4 Remarks . . . 196
8.4 Explicit Content Query: Mental Image Search by Visual Composition Formulation . . . 197
8.4.1 System Summary . . . 198
8.4.2 Visual Thesaurus Construction . . . 198
8.4.3 Symbolic Indexing, Boolean Search
and Range Query Mechanism . . . 199
8.4.4 Results . . . 201
8.4.5 Summary . . . 203
8.5 Conclusions . . . 203
References . . . 204
9 Combining Textual and Visual Information for Semantic Labeling of Images and Videos . . . . 205
Pınar Duygulu, Muhammet Bas¸tan, and Derya Ozkan 9.1 Introduction . . . 206
9.2 Semantic Labeling of Images . . . 207
9.3 Translation Approach . . . 210
9.3.1 Learning Correspondences Between Words and Regions . 211 9.3.2 Linking Visual Elements to Words in News Videos . . . 212
9.3.3 Translation Approach to Solve Video Association Problem . . . 213
9.3.4 Experiments on News Videos Data Set . . . 214
9.4 Naming Faces in News . . . 218
9.4.1 Integrating Names and Faces . . . 218
9.4.2 Finding Similarity of Faces . . . 219
9.4.3 Finding the Densest Component in the Similarity Graph . 220 9.4.4 Experiments . . . 221
9.5 Conclusion and Discussion . . . 223
References . . . 223
10 Machine Learning for Semi-structured Multimedia Documents: Application to Pornographic Filtering and Thematic Categorization. 227 Ludovic Denoyer and Patrick Gallinari 10.1 Introduction . . . 227
10.2 Previous Work . . . 229
10.2.1 Structured Document Classification . . . 230
10.2.2 Multimedia Documents . . . 231
10.3 Multimedia Generative Model . . . 231
10.3.1 Classification of Documents . . . 231
10.3.2 Generative Model . . . 232
10.3.3 Description . . . 232
10.4 Learning the Meta Model . . . 238
10.4.1 Maximization of Lstructure . . . 238
10.4.2 Maximization of Lcontent. . . 239
10.5 Local Generative Models for Text and Image . . . 239
10.5.1 Modelling a Piece of Text with Naive Bayes . . . 240
10.5.2 Image Model . . . 240
10.6 Experiments . . . 241
10.6.1 Models and Evaluation . . . 241
10.6.2 Corpora . . . 242
10.6.3 Results over the Pornographic Corpus . . . 243
10.6.4 Results over the Wikipedia Multimedia Categorization Corpus . . . 244
10.7 Conclusion . . . 246
References . . . 246
11 Classification and Clustering of Music for Novel Music Access Applications. . . . 249
Thomas Lidy and Andreas Rauber 11.1 Introduction . . . 250
11.2 Feature Extraction from Audio . . . 251
11.2.1 Low-Level Audio Features . . . 251
11.2.2 MPEG-7 Audio Descriptors . . . 252
11.2.3 MFCCs . . . 255
11.2.4 MARSYAS Features . . . 256
11.2.5 Rhythm Patterns . . . 258
11.2.6 Statistical Spectrum Descriptors . . . 259
11.2.7 Rhythm Histograms . . . 260
11.3 Automatic Classification of Music into Genres . . . 262
11.3.1 Evaluation Through Music Classification . . . 263
11.3.2 Benchmark Data Sets for Music Classification . . . 264
11.4 Creating and Visualizing Music Maps Based on Self-organizing Maps . . . 267
11.4.1 Class Visualization . . . 268
11.4.2 Hit Histograms . . . 269
11.4.3 U-Matrix . . . 270
11.4.4 P-Matrix . . . 271
11.4.5 U*-matrix . . . 272
11.4.6 Gradient Fields . . . 272
11.4.7 Component Planes . . . 273
11.4.8 Smoothed Data Histograms . . . 274
11.5 PlaySOM – Interaction with Music Maps . . . 276
11.5.1 Interface . . . 276
11.5.2 Interaction . . . 277
11.5.3 Playlist Creation . . . 278
11.6 PocketSOMPlayer – Music Retrieval on Mobile Devices . . . 280
11.6.1 Interaction . . . 281
11.6.2 Playing Scenarios . . . 282
11.6.3 Conclusion . . . 282
11.7 Conclusions . . . 282
References . . . 283
Index . . . 287
