Machine learning for multimedia content analysis

Library	Item Barcode	Call Number	Material Type	Item Category 1	Status
Searching... PSZ JB	30000010177439	QA76.575 G66 2007	Open Access Book	Book	Searching... Unknown
Searching... PSZ JB	30000010184072	QA76.575 G66 2007	Open Access Book	Book	Searching... Unknown

Challenges in complexity and variability of multimedia data have led to revolutions in machine learning techniques. Multimedia data, such as digital images, audio streams and motion video programs, exhibit richer structures than simple, isolated data items. A number of pixels in a digital image collectively conveys certain visual content to viewers. A TV video program consists of both audio and image streams that unfold the underlying story. To recognize the visual content of a digital image, or to understand the underlying story of a video program, we may need to label sets of pixels or groups of image and audio frames jointly.

Machine Learning for Multimedia Content Analysis introduces machine learning techniques that are particularly powerful and effective for modeling spatial, temporal structures of multimedia data and for accomplishing common tasks of multimedia content analysis. This book systematically covers these techniques in an intuitive fashion and demonstrates their applications through case studies. This volume uses a large number of figures to illustrate and visualize complex concepts, and provides insights into the characteristics of many algorithms through examinations of their loss functions and straightforward comparisons.

Machine Learning for Multimedia Content Analysis is designed for an academic and professional audience. Researchers will find this book an invaluable tool for applying machine learning techniques to multimedia content analysis. This volume is also suitable for practitioners in industry.

1 Introduction	p. 1
1.1 Basic Statistical Learning Problems	p. 2
1.2 Categorizations of Machine Learning Techniques	p. 4
1.2.1 Unsupervised vs. Supervised	p. 4
1.2.2 Generative Models vs. Discriminative Models	p. 4
1.2.3 Models for Simple Data vs. Models for Complex Data	p. 6
1.2.4 Model Identification vs. Model Prediction	p. 7
1.3 Multimedia Content Analysis	p. 8
Part I Unsupervised Learning
2 Dimension Reduction	p. 15
2.1 Objectives	p. 15
2.2 Singular Value Decomposition	p. 16
2.3 Independent Component Analysis	p. 20
2.3.1 Preprocessing	p. 23
2.3.2 Why Gaussian is Forbidden	p. 24
2.4 Dimension Reduction by Locally Linear Embedding	p. 26
2.5 Case Study	p. 30
Problems	p. 34
3 Data Clustering Techniques	p. 37
3.1 Introduction	p. 37
3.2 Spectral Clustering	p. 39
3.2.1 Problem Formulation and Criterion Functions	p. 39
3.2.2 Solution Computation	p. 42
3.2.3 Example	p. 46
3.2.4 Discussions	p. 50
3.3 Data Clustering by Non-Negative Matrix Factorization	p. 51
3.3.1 Single Linear NMF Model	p. 52
3.3.2 Bilinear NMF Model	p. 55
3.4 Spectral vs. NMF	p. 59
3.5 Case Study: Document Clustering Using Spectral and NMF Clustering Techniques	p. 61
3.5.1 Document Clustering Basics	p. 62
3.5.2 Document Corpora	p. 64
3.5.3 Evaluation Metrics	p. 64
3.5.4 Performance Evaluations and Comparisons	p. 65
Part II Generative Graphical Models
4 Introduction of Graphical Models	p. 73
4.1 Directed Graphical Model	p. 74
4.2 Undirected Graphical Model	p. 77
4.3 Generative vs. Discriminative	p. 79
4.4 Content of Part II	p. 80
5 Markov Chains and Monte Carlo Simulation	p. 81
5.1 Discrete-Time Markov Chain	p. 81
5.2 Canonical Representation	p. 84
5.3 Definitions and Terminologies	p. 88
5.4 Stationary Distribution	p. 91
5.5 Long Run Behavior and Convergence Rate	p. 94
5.6 Markov Chain Monte Carlo Simulation	p. 100
5.6.1 Objectives and Applications	p. 100
5.6.2 Rejection Sampling	p. 101
5.6.3 Markov Chain Monte Carlo	p. 104
5.6.4 Rejection Sampling vs. MCMC	p. 110
Problems	p. 112
6 Markov Random Fields and Gibbs Sampling	p. 115
6.1 Markov Random Fields	p. 115
6.2 Gibbs Distributions	p. 117
6.3 Gibbs - Markov Equivalence	p. 120
6.4 Gibbs Sampling	p. 123
6.5 Simulated Annealing	p. 126
6.6 Case Study: Video Foreground Object Segmentation by MRF	p. 133
6.6.1 Objective	p. 134
6.6.2 Related Works	p. 134
6.6.3 Method Outline	p. 135
6.6.4 Overview of Sparse Motion Layer Computation	p. 136
6.6.5 Dense Motion Layer Computation Using MRF	p. 138
6.6.6 Bayesian Inference	p. 140
6.6.7 Solution Computation by Gibbs Sampling	p. 141
6.6.8 Experimental Results	p. 143
Problems	p. 146
7 Hidden Markov Models	p. 149
7.1 Markov Chains vs. Hidden Markov Models	p. 149
7.2 Three Basic Problems for HMMs	p. 153
7.3 Solution to Likelihood Computation	p. 154
7.4 Solution to Finding Likeliest State Sequence	p. 158
7.5 Solution to HMM Training	p. 160
7.6 Expectation-Maximization Algorithm and its Variances	p. 162
7.6.1 Expectation-Maximization Algorithm	p. 162
7.6.2 Baum-Welch Algorithm	p. 164
7.7 Case Study: Baseball Highlight Detection Using HMMs	p. 167
7.7.1 Objective	p. 167
7.7.2 Overview	p. 167
7.7.3 Camera Shot Classification	p. 169
7.7.4 Feature Extraction	p. 172
7.7.5 Highlight Detection	p. 173
7.7.6 Experimental Evaluation	p. 174
Problems	p. 175
8 Inference and Learning for General Graphical Models	p. 179
8.1 Introduction	p. 179
8.2 Sum-product algorithm	p. 182
8.3 Max-product algorithm	p. 188
8.4 Approximate inference	p. 189
8.5 Learning	p. 191
Problems	p. 196
Part III Discriminative Graphical Models
9 Maximum Entropy Model and Conditional Random Field	p. 201
9.1 Overview of Maximum Entropy Model	p. 202
9.2 Maximum Entropy Framework	p. 204
9.2.1 Feature Function	p. 204
9.2.2 Maximum Entropy Model Construction	p. 205
9.2.3 Parameter Computation	p. 208
9.3 Comparison to Generative Models	p. 210
9.4 Relation to Conditional Random Field	p. 213
9.5 Feature Selection	p. 215
9.6 Case Study: Baseball Highlight Detection Using Maximum Entropy Model	p. 217
9.6.1 System Overview	p. 218
9.6.2 Highlight Detection Based on Maximum Entropy Model	p. 220
9.6.3 Multimedia Feature Extraction	p. 222
9.6.4 Multimedia Feature Vector Construction	p. 226
9.6.5 Experiments	p. 227
Problems	p. 232
10 Max-Margin Classifications	p. 235
10.1 Support Vector Machines (SVMs)	p. 236
10.1.1 Loss Function and Risk	p. 237
10.1.2 Structural Risk Minimization	p. 237
10.1.3 Support Vector Machines	p. 239
10.1.4 Theoretical Justification	p. 243
10.1.5 SVM Dual	p. 244
10.1.6 Kernel Trick	p. 245
10.1.7 SVM Training	p. 248
10.1.8 Further Discussions	p. 255
10.2 Maximum Margin Markov Networks	p. 257
10.2.1 Primal and Dual Problems	p. 257
10.2.2 Factorizing Dual Problem	p. 259
10.2.3 General Graphs and Learning Algorithm	p. 262
10.2.4 Max-Margin Networks vs. Other Graphical Models	p. 262
Problems	p. 264
A Appendix	p. 267
References	p. 269
Index	p. 275

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