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E-raamat: Multimedia Data Mining: A Systematic Introduction to Concepts and Theory

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(State University of New York, Binghamton, USA), (Yahoo Inc., Sunnyvale, California, USA)
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Collecting the latest developments in the field, Multimedia Data Mining: A Systematic Introduction to Concepts and Theory defines 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 and categorization.





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.
I Introduction 1
1 Introduction
3
1.1 Defining the Area
3
1.2 A Typical Architecture of a Multimedia Data Mining System
7
1.3 The Content and the Organization of This Book
8
1.4 The Audience of This Book
10
1.5 Further Readings
11
II Theory and Techniques 13
2 Feature and Knowledge Representation for Multimedia Data
15
2.1 Introduction
15
2.2 Basic Concepts
16
2.2.1 Digital Sampling
17
2.2.2 Media Types
18
2.3 Feature Representation
29
2.3.1 Statistical Feat Tires
23
2.3.2 Geometric Fearnre9
29
2.3.3 Meta Features
32
2.4 Knowledge Representation
32
2.4.1 Logic Representation
33
2.4.2 Semantic Networks
34
2.4.3 Frames
36
2.4.4 Constraints
38
2.4.5 Uncertainty Representation
41
2.5 Summary
44
3 Statistical Mining Theory and Techniques
45
3.1 Introduction
45
3.2 Bayesian Learning
47
3.2.1 Bayes Theorem
17
3.2.2 Bayes Optimal Classifier
19
3.2.3 Gibbs Algorithm
50
3.2.4 Naive Bayes Classifier
50
3.2.5 Bayesian Belief Networks
52
3.3 Probabilistic Latent Semantic Analysis
56
3.3.1 Latent Semantic Analysis
57
3.3.2 Probabilistic Extension to Latent Semantic Analysis
58
3.3.3 Model Fitting with the EM Algorithm
60
3.3.4 Latent Probability Space and Probabilistic Latent Semantic Analysis
61
3.3.5 Model Overfitting and Tempered EM
62
3.4 Latent Dirichlet Allocation for Discrete Data Analysis
63
3.4.1 Latent Dirichlet Allocation
64
3.4.2 Relationship to Other Latent Variable Models
66
3.4.3 Inference in LDA
69
3.4.4 Parameter Estimation in LDA
70
3.5 Hierarchical Dirichlet Process
72
3.6 Applications in Multimedia Data Mining
73
3.7 Support Vector Machines
74
3.8 Maximum Margin Learning for Structured Output Space
81
3.9 Boosting
88
3.10 Multiple Instance Learning
91
3.10.1 Establish the Mapping between the Word Space and the Image-VRep Space
93
3.10.2 Word-to-Image Querying
95
3.10.3 Image-to-Image Querying
95
3.10.4 Image-to-Word Querying
96
3.10.5 Multimodal Querying
96
3.10.6 Scalability Analysis
97
3.10.7 Adaptability Analysis
97
3.11 Semi-Supervised Learning
101
3.11.1 Supervised Learning
104
3.11.2 Semi-Supervised Learning
106
3.11.3 Semiparametric Regularized Least Squares
109
3.11.4 Seiniparametric Regularized Support Vector Machines
111
3.11.5 Semiparametric Regularization Algorithm
113
3.11.6 Transductive. Learning and Semi-Supervised Learning
113
3.11.7 Comparisons with Other Methods
114
3.12 Summary
115
4 Soft Computing Based Theory and Techniques
117
4.1 Introduction
117
4.2 Characteristics of the Paradigms of Soft Computing
118
4.3 Fuzzy Set Theory
119
4.3.1 Basic Concepts and Properties of Fuzzy Sets
119
4.3.2 Fuzzy Logic and Fuzzy Inference Rules
123
4.3.3 Fuzzy Set Application in Multimedia Data Mining
121
4.4. Artificial Neural Networks
125
4.4.1 Basic Architectures of Neural Networks
135
4.4.2 Supervised Learning in Neural Networks
131
4.4.3 Reinforcement Learning in Neural Networks
136
4.5 Genetic Algorithms
140
4.5.1 Genetic Algorithms in a Nutshell
140
4.5.2 Comparison of Conventional and Genetic Algorithms for an Extremum Search
145
4.6 Summary
150
III Multimedia Data Mining Application Examples 153
5 Image Database Modeling – Semantic Repository Training
155
5.1 Introduction
155
5.2 Background
156
5.3 Related Work
157
5.4 Image Features and Visual Dictionaries
159
5.4.1 Image Features
159
5.4.2 Visual Dictionary
160
5.5 α-Semantics Graph and Fuzzy Model for Repositories
163
5.5.1 a-Semantics Graph
163
5.5.2 Fuzzy Model for Repositories
166
5.6 Classification Based Retrieval Algorithm
168
5.7 Experiment Results
170
5.7.1 Classification Performance on a Controlled Database
170
5.7.2 Classification Based Retrieval Results
172
5.8 Summary
180
6 Image Database Modeling – Latent Semantic Concept Discovery
181
6.1 Introduction
181
6.2 Background and Related Work
182
6.3 Region Based Image Representation
185
6.3.1 Image Segmentation
185
6.3.2 Visual Token Catalog
188
6.4 Probabilistic Hidden Semantic Model
191
6.4.1 Probabilistic Database Model
191
6.4.2 Model Fitting with EM
192
6.4.3 Estimating the Number of Concepts
194
6.5 Posterior Probability Based Image Mining and Retrieval
194
6.6 Approach Analysis
196
6.7 Experiment al Results
199
6.8 Summary
205
7 A Multimodal Approach to Image Data Mining and Concept Discovery
209
7.1 Introduction
209
7.2 Background
210
7.3 Related Work
211
7.4 Probabilistic Semantic Model
213
7.4.1 Probabilistically Annotated Image Model
213
7.4.2 EM Based Procedure for Model Fitting
215
7.4.3 Estimating the Number of Concepts
216
7.5 Model Based Image Annotation and Multimodal Image Mining and Retrieval
217
7.5.1 Image Annotation and Image-to-Text Querying
217
7.5.2 Text-to-Image Querying
218
7.6 Experiments
219
7.6.1 Dataset and Feature Sets
220
7.6.2 Evaluation Metrics
221
7.6.3 Results of Automatic Image Annotation
221
7.6.4 Results of Single Word Text-to-Image Querying
224
7.6.5 Results of Image-to-Image Querying
224
7.6.6 Results of Performance Comparisons with Pure Text Indexing Methods
226
7.7 Summary
228
8 Concept Discovery and Mining in a Video Database
231
8.1 Introduction
231
8.2 Background
232
8.3 Related Work
233
8.4 Video Categorization
235
8.4.1 Naive Bayes Classifier
237
8.4.2 Maximum Entropy Classifier
238
8.4.3 Support Vector Machine Classifier
240
8.4.4 Combination of Meta Data arid Content Based Classifiers
241
8.5 Query Categorization
242
8.6 Experiments
244
8.6.1 Data Sets
244
8.6.2 Video Categorization Results
246
8.6.3 Query Categorization Results
251
8.6.4 Search Relevance Results
253
8.7 Summary
255
9 Concept Discovery and Mining in an Audio Database
257
9.1 Introduction
257
9.2 Background and Related Work
258
9.3 Feature Extraction
260
9.4 Classification Method
263
9.5 Experimental Results
263
9.6 Summary
269
References 271
Index 291
Zhang, Zhongfei; Zhang, Ruofei
Lisainfo e-raamatute kohta Lisainfo e-raamatute kohta
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