Multimedia database retrieval: technology and applications
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Cham
Springer International Publishing
[2014]
|
| Series: | Multimedia systems and applications
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | xii, 350 Seiten Illustrationen, Diagramme |
| ISBN: | 9783319117812 |
Staff View
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Record in the Search Index
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|---|---|
| adam_text | Contents
1 Introduction...................................................... 1
1.1 Objectives..................................................... 1
1.2 Multimedia Database Retrieval.................................. 2
1.2.1 Background............................................. 2
1.2.2 Challenges............................................. 2
1.2.3 The Development of M ultimedia Database
Retrieval Technology................................... 3
1.3 Technology Perspective......................................... 3
1.3.1 Human Centered Search and Retrieval.................... 3
1.3.2 Internet Scale Multimedia Analysis and Retrieval.... 5
1.3.3 Mobile Visual Search.................................. 7
1.3.4 Multimedia Retrieval in a Cloud Datacenter............ 8
1.3.5 Technologies of 2-D Video and 3-D Motion
Database Retrieval.................................... 10
1.4 Application Perspective....................................... 14
1.5 Organization of the Book.................................... 15
2 Kernel-Based Adaptive Image Retrieval Methods .................... 17
2.1 Introduction............ —.................................. 17
2.2 Kernel Methods in Adaptive Image Retrieval.................... 18
2.2.1 Adaptive Retrieval Framework.......................... 18
2.2.2 Query Adaptation Method............................. 19
2.2.3 Metric Adaptation Method.............................. 20
2.2.4 Query7 and Metric Adaptive Method .................... 21
2.2.5 Nonlinear Model-Based Adaptive Method................. 23
2.3 Single-Class Radial Basis Function Based Relevance Feedback .. 24
2.3.1 Center Selection...................................... 24
2.3.2 Width Selection..................................... 26
2.3.3 Experimental Result................................. 27
2.4 Multi-Class Radial Basis Function Method.................... 30
2.4.1 Local Model Network................................... 34
vil
Contents
viii
2.4.2 Learning Methods for the RBF Network.............. 35
2.4.3 Adaptive Radial-Basis Function Network............ 37
2.4.4 Gradient-Descent Procedure........................ 40
2.4.5 Fuzzy RBF Network with Soft Constraint............ 43
2.4.6 Experimental Result............................... 44
2.5 Bayesian Method for Fusion of Content and Context
in Adaptive Retrieval....................................... 47
2.5.1 Fusion of Content and Context..................... 47
2.5.2 Content-Based Likelihood Evaluation
in Short-Term Learning........................... 51
2.5.3 Context Model in Long-Term Learning.................. 52
2.5.4 Experimental Result............................... 54
2.6 Summary..................................................... 58
3 Self-adaptation in Image and Video Retrieval.................... 59
3.1 Introduction................................................ 59
3.2 Pseudo Relevance Feedback Methods........................... 60
3.2.1 Re-ranking Domain.................................... 60
3.2.2 Self-organizing Tree Map.......................... 62
3.2.3 Pseudo Labeling...................................... 65
3.2.4 Experimental Result.................................. 67
3.3 Re-ranking in Compressed Domains............................ 69
3.3.1 Descriptor in Discrete Cosine Transformation......... 69
3.3.2 Descriptor in Wavelet Based Coders................... 70
3.3.3 Experimental Result.................................. 74
3.4 Region-Based Re-ranking Method.............................. 80
3.4.1 Segmentation of the Region of Interest............... 82
3.4.2 Edge Flow Method..................................... 82
3.4.3 Knowledge-Based Automatic Region of interest......... 83
3.4.4 Pseudo-Relevance Feedback with Region of Interest. 84
3.4.5 Experimental Result.................................. 84
3.5 Video Re-ranking............................................ 87
3.5.1 Template Frequency Model Implementing
Bag-of-Words Model.................................. 87
3.5.2 Adaptive Cosine Network.............................. 89
3.5.3 Experimental Result.................................. 94
3.6 Summary................................................... 99
4 Interactive Mobile Visual Search and Recommendation
at Internet Scale.............................................. 101
4. i Introduction............................................... 101
4.2 BoW-Based Mobile Visual Search Using Various
Context Information........................................ 103
4.2.1 The Bag-oi-Word (BoW) Model......................... 104
4.2.2 Mobile Visual Search.............................. 106
Contents
IX
4.2.3 A Framework of Context-Aware Mobile
Visual Search......................................... 107
4.2.4 Context-Aware Visual Search Using the BoW Model----- 109
4.2.5 GPS Context-Based Filtering........................... 113
4.3 Mobile Visual Search System for Social Activities
Using Query Image Contextual Model.......................... 114
4.3.1 System Architecture................................... 116
4.3.2 User Interaction for Specifying Visual Intent......... 117
4.3.3 Social Activity Recommendations....................... 119
4.4 Experimental Result........................................... 120
4.4.1 Data, Settings, and Evaluation Metrics................ 120
4.4.2 Objective Evaluations................................. 121
4.4.3 Subjective Evaluation................................. 127
4.5 Summary....................................................... 129
5 Mobile Landmark Recognition....................................... 131
5.1 Introduction................................................ 131
5.2 Saliency Map Generation....................................... 132
5.3 Saliency-Aware Local Descriptor............................... 134
5.4 Saliency-Aware Scalable Vocabulary Tree....................... 135
5.4.1 Weighted Hierarchical Clustering ..................... 135
5.4.2 Saliency-Aware Bag-of-Word Representation ............ 136
5.5 Re-ranking Approach to Landmark Recognition................... 138
5.5.1 Building a Training Set via Ranking................... 138
5.5.2 Unsupervised Wrapper Feature Selection Method....... 138
5.5.3 Recognition Function.................................. 142
5.6 Experimental Result........................................... 142
5.7 Summary....................................................... 145
6 Image Retrieval from a Forensic Cartridge Case Database............ 147
6.1 Introduction.................................................. 147
6.1.1 Firearm Identification Procedure...................... 148
6.2 Image Registration Using Phase-Correlation Method............. 151
6.2.1 Parameter Estimation for Translation.................. 151
6.2.2 Parameter Estimation for Rotation..................... 152
6.2.3 Parameter Estimation for Scaling...................... 154
6.2.4 Registration Accuracy................................. 155
6.3 ECA-Based Image-Matching Method............................... 158
6.3.1 Local Normalization with Cross-Covariance Function ... 160
6.3.2 Edge-Density Measurement.............................. 162
6.4 Experimental Result........................... ............. 163
6.5 Summary....................................................... 166
7 Indexing., Object Segmentation, and Event Detection
in News and Sports Videos......................................... 169
7.1 Introduction.................................................. 169
Contents
7.2 Video Parsing in Compressed Domain........................... 171
7.2.1 Conventional Method.................................. 171
7.2.2 Twin Window Amplification Method..................... 172
7.2.3 Demonstration........................................ 174
7.3 News Video Retrieval......................................... 175
7.3.1 Characterization of News Video Units ................ 175
7.3.2 Indexing and Retrieval of News Video................. 178
7.3.3 Demonstration........................................ 180
7.4 Segmentation of Video Objects................................ 182
7.4.1 Graph Cut Video Segmentation......................... 182
7.4.2 Object Segmentation.................................. 187
7.4.3 Histogram of Oriented Gradients...................... 188
7.5 Segmentation of Face Object Under Illumination Variations... 191
7.5.1 Automatic Face Detection using Optimal Adaptive Correlation Method with Local
Normalization........................................ 193
7.5.2 Experimental Result.................................. 197
7.6 Play Event NFL Video Classification Using
MPEG-7 and MFCC Features..................................... 200
7.6.1 Localization of Play Events........................ 201
7.6.2 Classification of American Football Events........... 205
7.6.3 Experimental Results................................. 209
7.7 Summary...................................................... 210
Adaptive Retrieval in a P2P Cloud Datacenter......................
8.1 1 ntroduction................................................
8.2 Distributed Database System.................................
8.2.1 Cloud Datacenter....................................
8.2.2 Application of a Multimedia Retrieval System
in a P2P Datacenter.................................
8.3 Adaptive Image Retrieval in a Self-organizing
Chord P2P Network...........................................
8.3.1 System Architecture.................................
8.3.2 Indexing of Nodes and Data Items on the
Distributed Hash Table..............................
8.3.3 Query Processing on the P2P Network.................
8.4 Social Network Image Retrieval Using
Pseudo-Relevance Feedback...................................
8.4.1 Social Network Discoveiy............................
8.4.2 Query Within the Social Network.....................
8.4.3 Pseudo Relevance Feedback in the Distributed
Database System.....................................
8.4.4 Experimental Result.................................
8.5 Video Re-ranking on the Social P2P Network..................
8.5.1 System Architecture ................................
8.5.2 Video Indexing on the P2P Network...................
213
213
214
214
215
217
217
218
221
229
233
237
238 238
Contents
xi
8.5.3 Re-ranking Approach to P2P Video Retrieval.......... 239
8.5.4 Experimental Result................................... 243
8.6 Summary....................................................... 246
9 Scalable Video Genre Classification and Event Detection............ 247
9.1 I ntroduction................................................. 247
9.1.1 Overview.............................................. 249
9.2 Video Representation and Genre Categorization .............. 252
9.2.1 Related Work ...............7....................... 252
9.2.2 Bottom-Up Codebook Generation........... ............. 254
9.2.3 Low-Level Genre Categorization ....................... 256
9.3 High-Level Event Detection Using Middle-Level View
as Agent.................................................... 256
9.3. f Related Work.......................................... 257
9.3.2 Middle-Level Unsupervised View Classification......... 259
9.3.3 High-Level Event Detection ........................... 264
9.4 Experimental Result........................................... 268
9.4.1 Genre Categorization Using K-Nearest
Neighbor Classifier................................... 270
9.4.2 Middle-Level View Classification Using
Supervised SVM and Un supervised PLSA............... 273
9.4.3 Event Detection Using Coarse-to-Fine Scheme........... 275
9.5 Summary....................................................... 278
10 Audio-Visual Fusion lor Film Database Retrieval
and Classification................................................ 279
10.1 Introduction.................................................. 279
10.2 Audio Content Characterization ............................ 280
10.2.1 Finite Mixture Model.................................. 281
10.2.2 Laplacian Mixture Model and Parameter Estimation.... 282
10.2.3 Comparison of Gaussian Mixture Model and
Laplacian Mixture Model............................... 284
10.2.4 Feature Extraction from Audio Signal.... ............. 286
10.2.5 Performance of Video Retrieval Using Audio Indexing .. 287
10.3 Visual Content Characterization............................. 289
10.3. i ^sual indexing Algorithm............................ 289
103.2 Performance Comparison for Retrievals from
Movie Database................................. 290
10.4 Audio-Visual Fusion ........................................ 294
10.4.1 Decision Fusion Model............................. 295
10.4.2 Support Vector Machine Learning....................... 296
10.4.3 Implementation of Support Vector Machine............. 298
10.4.4 Results of Movie Clip Classification ............... 300
10.5 Summary.............................-....................... 303
11 Motion Database Retrieval with Application to Gesture
Recognition in a Virtual Reality Dance Training System............ 305
11.1 Introduction ................. —.............................. 305
Contents
xii
11.2 Dance Training System......................................... 306
11.3 Spherical Self-organizing Map (SSOM).......................... 309
11.4 Characterization of Dance Gesture Using Spherical
Self-organizing Map ......................................... 311
11.5 Trajectory Analysis.......................................... 312
11.5.1 Sparse Code of Spherical Self-organizing Map......... 314
11.5.2 Posture Occurrence................................... 315
11.5.3 Posture Transition and Posture Transition
Sparse Code........................................... 316
11.5.4 Performance Comparison............................... 317
31.6 Online Gesture Recognition and Segmentation.............;.... 319
11.7 Trajectory Analysis on the Multicodebook SSOM
Using Hidden Markov Model.................................. 321
11.7.1 The Self-organizing Map Distortion Measurement...... 322
1 1.7.2 The Hidden Markov Models of Gesture.................. 325
11.7.3 Obtaining Learning Parameters........................ 328
11.7.4 Experimental Result.................................. 329
11.8 Summary....................................................... 333
References............................................................ 335
|
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| id | DE-604.BV043372043 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T07:24:11Z |
| institution | BVB |
| isbn | 9783319117812 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-028790944 |
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| physical | xii, 350 Seiten Illustrationen, Diagramme |
| publishDate | 2014 |
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| publisher | Springer International Publishing |
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| series2 | Multimedia systems and applications |
| spelling | Muneesawang, Paisarn aut Multimedia database retrieval technology and applications Paisarn Muneesawang, Ning Zhang, Ling Guan Cham Springer International Publishing [2014] xii, 350 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Multimedia systems and applications Informationssystem (DE-588)4072806-7 gnd rswk-swf Computervision (DE-588)4207417-4 gnd rswk-swf Multimedia (DE-588)4192358-3 gnd rswk-swf Datenbankverwaltung (DE-588)4389357-0 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Datenbankverwaltung (DE-588)4389357-0 s Informationssystem (DE-588)4072806-7 s Maschinelles Lernen (DE-588)4193754-5 s Multimedia (DE-588)4192358-3 s Computervision (DE-588)4207417-4 s DE-604 Zhang, Ning aut Guan, Ling aut Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028790944&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Muneesawang, Paisarn Zhang, Ning Guan, Ling Multimedia database retrieval technology and applications Informationssystem (DE-588)4072806-7 gnd Computervision (DE-588)4207417-4 gnd Multimedia (DE-588)4192358-3 gnd Datenbankverwaltung (DE-588)4389357-0 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4072806-7 (DE-588)4207417-4 (DE-588)4192358-3 (DE-588)4389357-0 (DE-588)4193754-5 |
| title | Multimedia database retrieval technology and applications |
| title_auth | Multimedia database retrieval technology and applications |
| title_exact_search | Multimedia database retrieval technology and applications |
| title_full | Multimedia database retrieval technology and applications Paisarn Muneesawang, Ning Zhang, Ling Guan |
| title_fullStr | Multimedia database retrieval technology and applications Paisarn Muneesawang, Ning Zhang, Ling Guan |
| title_full_unstemmed | Multimedia database retrieval technology and applications Paisarn Muneesawang, Ning Zhang, Ling Guan |
| title_short | Multimedia database retrieval |
| title_sort | multimedia database retrieval technology and applications |
| title_sub | technology and applications |
| topic | Informationssystem (DE-588)4072806-7 gnd Computervision (DE-588)4207417-4 gnd Multimedia (DE-588)4192358-3 gnd Datenbankverwaltung (DE-588)4389357-0 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Informationssystem Computervision Multimedia Datenbankverwaltung Maschinelles Lernen |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028790944&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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