Multimedia data mining: a systematic introduction to concepts and theory
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Boca Raton [u.a.]
CRC Press
2009
|
| Schriftenreihe: | Chapman & Hall/CRC data mining and knowledge discovery series
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Includes bibliographical references and index |
| Beschreibung: | XXVI, 293 S. graf. Darst. |
| ISBN: | 9781584889663 |
Internformat
MARC
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|---|---|---|---|
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| 008 | 090121s2009 xxu|||| |||| 00||| eng d | ||
| 010 | |a 2008039398 | ||
| 020 | |a 9781584889663 |c hardcover : alk. paper |9 978-1-58488-966-3 | ||
| 035 | |a (OCoLC)144225448 | ||
| 035 | |a (DE-599)BVBBV035261928 | ||
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| 245 | 1 | 0 | |a Multimedia data mining |b a systematic introduction to concepts and theory |c Zhongfei Zhang ; Ruofei Zhang |
| 264 | 1 | |a Boca Raton [u.a.] |b CRC Press |c 2009 | |
| 300 | |a XXVI, 293 S. |b graf. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Chapman & Hall/CRC data mining and knowledge discovery series | |
| 500 | |a Includes bibliographical references and index | ||
| 650 | 4 | |a Multimedia systems | |
| 650 | 4 | |a Data mining | |
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| 689 | 0 | |5 DE-604 | |
| 700 | 1 | |a Zhang, Ruofei |e Verfasser |4 aut | |
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Datensatz im Suchindex
| _version_ | 1804138545398415360 |
|---|---|
| adam_text | Contents
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
..................... 22
2.3.1
Statistical Features
.................... 23
2.3.2
Geometric Features
.................... 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
...................... 47
3.2.2
Bayes
Optimal Classifier
................. 49
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
Xli
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 Se¬
mantic Analysis
...................... 61
3.3.5
Model Overrating 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
Semiparametric 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
Soft Computing Based Theory and Techniques
117
4.1
Introduction
........................... 117
4.2
Characteristics of the Paradigms of Soft Computing
..... 118
4.3
Fnzzy 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
. . 124
4.4
Artificial Neural Networks
................... 125
4.4.1
Basic Architectures of Neural Networks
........ 125
4.4.2
Supervised Learning in Neural Networks
........ 131
Xlii
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 Extrermim 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
a-Semantics Graph and Fuzzy Model for Repositories
.... 163
5.5.1
α
-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 Dis¬
covery
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
Experimental 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
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
and Content Based Classi¬
fiers
............................ 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
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
|
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| isbn | 9781584889663 |
| language | English |
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| spelling | Zhang, Zhongfei Verfasser aut Multimedia data mining a systematic introduction to concepts and theory Zhongfei Zhang ; Ruofei Zhang Boca Raton [u.a.] CRC Press 2009 XXVI, 293 S. graf. Darst. txt rdacontent n rdamedia nc rdacarrier Chapman & Hall/CRC data mining and knowledge discovery series Includes bibliographical references and index Multimedia systems Data mining Multimedia (DE-588)4192358-3 gnd rswk-swf Data Mining (DE-588)4428654-5 gnd rswk-swf Multimedia (DE-588)4192358-3 s Data Mining (DE-588)4428654-5 s DE-604 Zhang, Ruofei Verfasser aut Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017067438&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Zhang, Zhongfei Zhang, Ruofei Multimedia data mining a systematic introduction to concepts and theory Multimedia systems Data mining Multimedia (DE-588)4192358-3 gnd Data Mining (DE-588)4428654-5 gnd |
| subject_GND | (DE-588)4192358-3 (DE-588)4428654-5 |
| title | Multimedia data mining a systematic introduction to concepts and theory |
| title_auth | Multimedia data mining a systematic introduction to concepts and theory |
| title_exact_search | Multimedia data mining a systematic introduction to concepts and theory |
| title_full | Multimedia data mining a systematic introduction to concepts and theory Zhongfei Zhang ; Ruofei Zhang |
| title_fullStr | Multimedia data mining a systematic introduction to concepts and theory Zhongfei Zhang ; Ruofei Zhang |
| title_full_unstemmed | Multimedia data mining a systematic introduction to concepts and theory Zhongfei Zhang ; Ruofei Zhang |
| title_short | Multimedia data mining |
| title_sort | multimedia data mining a systematic introduction to concepts and theory |
| title_sub | a systematic introduction to concepts and theory |
| topic | Multimedia systems Data mining Multimedia (DE-588)4192358-3 gnd Data Mining (DE-588)4428654-5 gnd |
| topic_facet | Multimedia systems Data mining Multimedia Data Mining |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017067438&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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