Multimedia Mining

A Highway to Intelligent Multimedia Documents

By: Chabane Djeraba (Editor)

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Hardcover | 30 November 2002

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252 Pages

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23.5 x 15.88 x 2.54

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The conjunction of knowledge discovery and multimedia documents presents a very important challenge. This new field of knowledge discovery in multimedia documents, dealing with non-structured information such as texts, images, videos, audio and virtual data, requires the collaboration of experts in many areas of interest revolving around document management systems, data mining, visual computing, signal processing, multimedia data and machine learning. This work brings together experts in digital media content analysis, state-of-art data mining and knowledge discovery in multimedia database systems, knowledge engineers and domain experts from diverse applied disciplines. Multimedia documents are ubiquitous and often required, if not essential, in many applications today. This phenomenon has made multimedia documents widespread and extremely large. There are tools for managing and searching within these collections, but the need for tools to extract hidden useful knowledge embedded within multimedia objects is becoming pressing and central for many decision-making applications. The tools needed today are tools for discovering relationships between objects or segments within multimedia document components, such as classifying images based on their content, extracting patterns in sound, categorizing speech and music and recognizing and tracking objects in video streams.

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Non-Fiction Computing & I.T.Databases Data Mining Graphical & Digital Media Applications Medicine Medicine in General Computer Programming & Software Development Algorithms & Data Structures Computer Science

Preface	p. XIII
Featured Chapters	p. 1
IMET: Image Mining for Typhoon Analysis	p. 7
Introduction	p. 7
Typhoon from an Informatics Perspective	p. 8
Meteorological Background	p. 8
Challenges to Informatics	p. 8
Typhoon Image Collection	p. 10
Typhoon Images from a CBIR Perspective	p. 11
Scientific Application and Narrow Domain	p. 11
Time-Series Data in Sequences	p. 11
Variability of Cloud Patterns	p. 11
Characteristic Scale	p. 12
Invariance	p. 12
Multi-spectral Images	p. 12
Metadata and Annotation	p. 13
Representation of the Typhoon	p. 13
Component-based Representation	p. 13
Multi-scale Representation	p. 15
Shape-based Representation	p. 15
Image Mining	p. 15
Finding Typical Patterns	p. 15
Image Mining and CBIR	p. 17
Image Mining Environment for Typhoon Analysis and Prediction	p. 18
System Architecture	p. 18
Query Language	p. 19
Exploration with IMET	p. 21
Search by Metadata	p. 21
Spatial Data Mining	p. 21
Comparison of Multiple Typhoon Sequences	p. 22
Temporal Data Mining	p. 22
Conclusion	p. 23
Acknowledgment	p. 24
References	p. 24
Discovering Patterns with and within Images	p. 27
Introduction	p. 27
What is Image Mining?	p. 27
Image Mining versus Content-based Image Retrieval	p. 28
Issues and Challenges	p. 30
Image Mining Techniques	p. 32
Content-based and non-content-based image mining	p. 32
Image Mining Techniques	p. 33
Classification of images and/or image content	p. 33
Image clustering	p. 34
Association Rule Mining within images	p. 35
Pre-processing images presents a challenge	p. 37
Conclusion	p. 39
References	p. 40
A System Supporting Semantics Retrieval	p. 43
Introduction	p. 43
Scenery Analyzer: System Framework	p. 45
A Hierarchical Representation for Low-Level Features	p. 47
Extracting Semantic Features	p. 50
Classifying Textons	p. 51
Clustering Textons	p. 53
Rendering Semantic Regions	p. 53
Case Study of Semantic Features	p. 54
Background and Sky	p. 54
Ground	p. 54
Wave	p. 55
Green Tree	p. 55
Building	p. 55
Conclusion	p. 57
References	p. 58
Techniques for Color-Based Image Retrieval	p. 61
Introduction	p. 61
Color-Spaces	p. 63
Color-based image description	p. 64
Static reduction methods	p. 65
Dynamic reduction methods	p. 65
Visual features extraction and representation	p. 67
Global representations	p. 67
Partition-based representations	p. 68
Regional representations	p. 68
Distance Function	p. 69
Similarity Search	p. 70
Filtering	p. 71
Spatial access methods - SAMs	p. 72
Metric access methods - MAMs	p. 72
Approximate and non-metric methods	p. 72
Existing CBIR approaches	p. 73
Global approaches	p. 73
Partition-based approaches	p. 75
Regional approaches	p. 76
Open problems	p. 78
Summary	p. 79
Acknowledgment	p. 79
References	p. 80
Recovering in Video Documents	p. 83
Introduction	p. 83
Temporal video segmentation	p. 85
Computation of optical flow	p. 86
Orthogonal Dynamic Programming	p. 87
Building and selection of trajectories	p. 88
Turning sequences of vector fields into sets of trajectories	p. 90
Building trajectories for a reference frame	p. 90
Building trajectories using several reference frames	p. 91
Selecting reliable and optimally spread trajectories	p. 92
Use of the gradient of the image intensity	p. 92
Module of the gradient of vector field	p. 93
Shi-Tomasi-Kanade eigenvalues	p. 93
Conservation of a transported window	p. 94
Selection of trajectories	p. 95
Camera model	p. 95
Pinhole camera model	p. 95
Intrinsic parameters	p. 96
Extrinsic parameters	p. 96
Video sequence camera parameters	p. 97
Recovery of camera motion without parallax	p. 99
Search for homographies and background regions	p. 99
Search for camera parameters	p. 102
Search for local parameters	p. 103
Search for global parameters	p. 104
Search for all parameters	p. 105
Sample results	p. 105
Recovery of camera motion with parallax	p. 107
Integration	p. 109
Conclusion	p. 110
Acknowledgments	p. 110
References	p. 110
Mining of Video Database	p. 113
Introduction	p. 113
Semantics-Sensitive Video Database Model	p. 115
Video Analysis and Feature Extraction	p. 119
Semantics-Sensitive Video Classification	p. 121
Hierarchical Database Indexing and Access	p. 129
Conclusions	p. 135
Acknowledgement	p. 136
References	p. 136
Medical Multimedia Databases	p. 139
Introduction	p. 139
Review of Medical Multimodality and Multimedia Systems	p. 140
Content-based medical image retrieval techniques	p. 141
Brain image segmentation techniques	p. 142
Intensity-based segmentation methods	p. 142
Texture-based segmentation methods	p. 143
Model-based segmentation methods	p. 144
Segmentation and measurements in MR images	p. 144
Multimedia and multimodality based medical systems	p. 146
The MEDIMAGE System	p. 147
The MEDIMAGE system databases	p. 148
The MEDIMAGE MR image processing tools	p. 149
The MEDIMAGE database management tools	p. 149
Results obtained with the MEDIMAGE system	p. 150
The MEDIMAGE system summary	p. 153
The Epilepsy System	p. 153
The Epilepsy system architecture	p. 153
The Epilepsy system methods	p. 155
Results obtaining with the Epilepsy system	p. 155
Conclusions	p. 157
References	p. 158
An Object Approach for Web Presentations	p. 161
Introduction	p. 161
The V-STORM System	p. 162
The AROM System	p. 164
Coupling AROM and V-STORM	p. 165
An AROM Model for Multimedia Presentations	p. 165
Building a multimedia presentation	p. 168
Benefits of the AVS model	p. 169
The Template model	p. 170
Template definition	p. 170
Spatial Description	p. 171
Temporal Description	p. 172
Translation into AROM	p. 173
From Templates to SMIL Presentations	p. 173
Related Works	p. 175
Conclusion	p. 176
References	p. 177
Web Multiform Data Structuring	p. 179
Introduction	p. 179
Related work	p. 181
Data integration	p. 181
XML Mapping	p. 182
XML query languages	p. 182
UML conceptual model	p. 183
XML logical model	p. 185
XML physical model	p. 187
Transformation algorithm	p. 187
Implementation	p. 188
Architecture	p. 188
Attribute extraction	p. 189
XML document generation	p. 190
Output	p. 190
Mapping into a relational database	p. 193
Conclusion and future issues	p. 193
References	p. 194
Media Annotation	p. 197
Introduction	p. 197
Generation of describers	p. 197
Segmentation and text annotation	p. 197
Images and annotations	p. 200
Audio segmentation and annotation	p. 201
Segmentation and annotation of animated image	p. 202
Dimensions	p. 204
Querying	p. 205
Conclusion	p. 208
References	p. 208
Audio Content-based Classification	p. 213
Introduction	p. 213
Framework of semantic classes	p. 215
Silence Description	p. 216
Speech Description	p. 217
Music Description	p. 217
Noise Description	p. 218
Classification method	p. 218
Retrieval	p. 221
Experimentation	p. 222
Format of Sound data storage	p. 222
Evaluation method	p. 222
Data sets	p. 223
Sample of an audio material	p. 223
Results and analysis	p. 224
Comparison with related works	p. 225
Conclusion	p. 226
Acknowledgment	p. 227
References	p. 227
Index	p. 229
Table of Contents provided by Ingram. All Rights Reserved.

Multimedia Mining

A Highway to Intelligent Multimedia Documents

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Hardcover

Shipping

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