Multimedia Data Mining: A Systematic Introduction to Concepts and Theory

by Zhang; Zhongfei

• ISBN13: 9781584889663
• ISBN10: 1584889667
• Format: Hardcover
• Copyright: 2008-12-02
• Publisher: Chapman & Hall/

Summary

Collecting the latest developments in the field, Multimedia Data Mining: A Systematic Introduction to Concepts and Theorydefines multimedia data mining, its theory, and its applications. Two of the most active researchers in multimedia data mining explore how this young area has rapidly developed in recent years.The book first discusses the theoretical foundations of multimedia data mining, presenting commonly used feature representation, knowledge representation, statistical learning, and soft computing techniques. It then provides application examples that showcase the great potential of multimedia data mining technologies. In this part, the authors show how to develop a semantic repository training method and a concept discovery method in an imagery database. They demonstrate how knowledge discovery helps achieve the goal of imagery annotation. The authors also describe an effective solution to large-scale video search, along with an application of audio data classification andcategorization.This novel, self-contained book examines how the merging of multimedia and data mining research can promote the understanding and advance the development of knowledge discovery in multimedia data.

Table of Contents

Introduction	p. 1
Introduction	p. 3
Defining the Area	p. 3
A Typical Architecture of a Multimedia Data Mining System	p. 7
The Content and the Organization of This Book	p. 8
The Audience of This Book	p. 10
Further Readings	p. 11
Theory and Techniques	p. 13
Feature and Knowledge Representation for Multimedia Data	p. 15
Introduction	p. 15
Basic Concepts	p. 16
Digital Sampling	p. 17
Media Types	p. 18
Feature Representation	p. 22
Statistical Features	p. 23
Geometric Features	p. 29
Meta Features	p. 32
Knowledge Representation	p. 32
Logic Representation	p. 33
Semantic Networks	p. 34
Frames	p. 36
Constraints	p. 38
Uncertainty Representation	p. 41
Summary	p. 44
Statistical Mining Theory and Techniques	p. 45
Introduction	p. 45
Bayesian Learning	p. 47
Bayes Theorem	p. 47
Bayes Optimal Classifier	p. 49
Gibbs Algorithm	p. 50
Naive Bayes Classifier	p. 50
Bayesian Belief Networks	p. 52
Probabilistic Latent Semantic Analysis	p. 56
Latent Semantic Analysis	p. 57
Probabilistic Extension to Latent Semantic Analysis	p. 58
Model Fitting with the EM Algorithm	p. 60
Latent Probability Space and Probabilistic Latent Semantic Analysis	p. 61
Model Overfitting and Tempered EM	p. 62
Latent Dirichlet Allocation for Discrete Data Analysis	p. 63
Latent Dirichlet Allocation	p. 64
Relationship to Other Latent Variable Models	p. 66
Inference in LDA	p. 69
Parameter Estimation in LDA	p. 70
Hierarchical Dirichlet Process	p. 72
Applications in Multimedia Data Mining	p. 73
Support Vector Machines	p. 74
Maximum Margin Learning for Structured Output Space	p. 81
Boosting	p. 88
Multiple Instance Learning	p. 91
Establish the Mapping between the Word Space and the Image-VRep Space	p. 93
Word-to-Image Querying	p. 95
Image-to-Image Querying	p. 95
Image-to-Word Querying	p. 96
Multimodal Querying	p. 96
Scalability Analysis	p. 97
Adaptability Analysis	p. 97
Semi-Supervised Learning	p. 101
Supervised Learning	p. 104
Semi-Supervised Learning	p. 106
Semiparametric Regularized Least Squares	p. 109
Semiparametric Regularized Support Vector Machines	p. 111
Semiparametric Regularization Algorithm	p. 113
Transductive Learning and Semi-Supervised Learning	p. 113
Comparisons with Other Methods	p. 114
Summary	p. 115
Soft Computing Based Theory and Techniques	p. 117
Introduction	p. 117
Characteristics of the Paradigms of Soft Computing	p. 118
Fuzzy Set Theory	p. 119
Basic Concepts and Properties of Fuzzy Sets	p. 119
Fuzzy Logic and Fuzzy Inference Rules	p. 123
Fuzzy Set Application in Multimedia Data Mining	p. 124
Artificial Neural Networks	p. 125
Basic Architectures of Neural Networks	p. 125
Supervised Learning in Neural Networks	p. 131
Reinforcement Learning in Neural Networks	p. 136
Genetic Algorithms	p. 140
Genetic Algorithms in a Nutshell	p. 140
Comparison of Conventional and Genetic Algorithms for an Extremum Search	p. 145
Summary	p. 150
Multimedia Data Mining Application Examples	p. 153
Image Database Modeling - Semantic Repository Training	p. 155
Introduction	p. 155
Background	p. 156
Related Work	p. 157
Image Features and Visual Dictionaries	p. 159
Image Features	p. 159
Visual Dictionary	p. 160
[alpha]-Semantics Graph and Fuzzy Model for Repositories	p. 163
[alpha]-Semantics Graph	p. 163
Fuzzy Model for Repositories	p. 166
Classification Based Retrieval Algorithm	p. 168
Experiment Results	p. 170
Classification Performance on a Controlled Database	p. 170
Classification Based Retrieval Results	p. 172
Summary	p. 180
Image Database Modeling - Latent Semantic Concept Discovery	p. 181
Introduction	p. 181
Background and Related Work	p. 182
Region Based Image Representation	p. 185
Image Segmentation	p. 185
Visual Token Catalog	p. 188
Probabilistic Hidden Semantic Model	p. 191
Probabilistic Database Model	p. 191
Model Fitting with EM	p. 192
Estimating the Number of Concepts	p. 194
Posterior Probability Based Image Mining and Retrieval	p. 194
Approach Analysis	p. 196
Experimental Results	p. 199
Summary	p. 205
A Multimodal Approach to Image Data Mining and Concept Discovery	p. 209
Introduction	p. 209
Background	p. 210
Related Work	p. 211
Probabilistic Semantic Model	p. 213
Probabilistically Annotated Image Model	p. 213
EM Based Procedure for Model Fitting	p. 215
Estimating the Number of Concepts	p. 216
Model Based Image Annotation and Multimodal Image Mining and Retrieval	p. 217
Image Annotation and Image-to-Text Querying	p. 217
Text-to-Image Querying	p. 218
Experiments	p. 219
Dataset and Feature Sets	p. 220
Evaluation Metrics	p. 221
Results of Automatic Image Annotation	p. 221
Results of Single Word Text-to-Image Querying	p. 224
Results of Image-to-Image Querying	p. 224
Results of Performance Comparisons with Pure Text Indexing Methods	p. 226
Summary	p. 228
Concept Discovery and Mining in a Video Database	p. 231
Introduction	p. 231
Background	p. 232
Related Work	p. 233
Video Categorization	p. 235
Naive Bayes Classifier	p. 237
Maximum Entropy Classifier	p. 238
Support Vector Machine Classifier	p. 240
Combination of Meta Data and Content Based Classifiers	p. 241
Query Categorization	p. 242
Experiments	p. 244
Data Sets	p. 244
Video Categorization Results	p. 246
Query Categorization Results	p. 251
Search Relevance Results	p. 253
Summary	p. 255
Concept Discovery and Mining in an Audio Database	p. 257
Introduction	p. 257
Background and Related Work	p. 258
Feature Extraction	p. 260
Classification Method	p. 263
Experimental Results	p. 263
Summary	p. 269
References	p. 271
Index	p. 291
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