Data Management for Multimedia Retrieval

Multimedia data require specialized management techniques because the representations of color, time, semantic concepts, and other underlying information can be drastically different from one another. The user's subjective judgment can also have significant impact on what data or features are relevant in a given context. These factors affect both the performance of the retrieval algorithms and their effectiveness. This textbook on multimedia data management techniques offers a unified perspective on retrieval efficiency and effectiveness. It provides a comprehensive treatment, from basic to advanced concepts, that will be useful to readers of different levels, from advanced undergraduate and graduate students to researchers and to professionals. After introducing models for multimedia data (images, video, audio, text, and web) and for their features, such as color, texture, shape, and time, the book presents data structures and algorithms that help store, index, cluster, classify, and access common data representations. The authors also introduce techniques, such as relevance feedback and collaborative filtering, for bridging the "semantic gap" and present the applications of these to emerging topics, including web and social networking.

K. Seltuk Candan is a Professor of Computer Science and Engineering at Arizona State University.
Maria Luisa Sapino is a Professor in the Department of Computer Science at the University of Torino.

Preface	p. ix
Introduction: Multimedia Applications and Data Management Requirements	p. 1
Heterogeneity	p. 1
Imprecision and Subjectivity	p. 8
Components of a Multimedia Database Management System	p. 12
Summary	p. 19
Models for Multimedia Data	p. 20
Overview of Traditional Data Models	p. 21
Multimedia Data Modeling	p. 32
Models of Media Features	p. 34
Multimedia Query Languages	p. 92
Summary	p. 98
Common Representations of Multimedia Features	p. 99
Vector Space Models	p. 99
Strings and Sequences	p. 109
Graphs and Trees	p. 111
Fuzzy Models	p. 115
Probabilistic Models	p. 123
Summary	p. 142
Feature Quality and Independence: Why and How?	p. 143
Dimensionality Curse	p. 144
Feature Selection	p. 145
Mapping from Distances to a Multidimensional Space	p. 167
Embedding Data from One Space into Another	p. 172
Summary	p. 180
Indexing, Search, and Retrieval of Sequences	p. 181
Inverted Files	p. 181
Signature Files	p. 184
Signature-and Inverted-File Hybrids	p. 190
Sequence Matching	p. 191
Approximate Sequence Matching	p. 195
Wildcard Symbols and Regular Expressions	p. 202
Multiple Sequence Matching and Filtering	p. 204
Summary	p. 206
Indexing, Search, and Retrieval of Graphs and Trees	p. 208
Graph Matching	p. 208
Tree Matching	p. 212
Link/Structure Analysis	p. 222
Summary	p. 233
Indexing, Search, and Retrieval of Vectors	p. 235
Space-Filling Curves	p. 238
Multidimensional Index Structures	p. 244
Summary	p. 270
Clustering Techniques	p. 271
Quality of a Clustering Scheme	p. 272
Graph-Based Clustering	p. 275
Iterative Methods	p. 280
Multiconstraint Partitioning	p. 286
Mixture Model Based Clustering	p. 287
Online Clustering with Dynamic Evidence	p. 288
Self-Organizing Maps	p. 290
Co-clustering	p. 292
Summary	p. 296
Classification	p. 297
Decision Tree Classification	p. 297
k-Nearest Neighbor Classifiers	p. 301
Support Vector Machines	p. 301
Rule-Based Classification	p. 308
Fuzzy Rule-Based Classification	p. 311
Bayesian Classifiers	p. 314
Hidden Markov Models	p. 316
Model Selection: Overfitting Revisited	p. 322
Boosting	p. 324
Summary	p. 326
Ranked Retrieval	p. 327
k-Nearest Objects Search	p. 328
Top-k Queries	p. 337
Skylines	p. 360
Optimization of Ranking Queries	p. 373
Summary	p. 379
Evaluation of Retrieval	p. 380
Precision and Recall	p. 381
Single-Valued Summaries of Precision and Recall	p. 381
Systems with Ranked Results	p. 383
Single-Valued Summaries of Precision-Recall Curve	p. 384
Evaluating Systems Using Ranked and Graded Ground Truths	p. 386
Novelty and Coverage	p. 390
Statistical Significance of Assessments	p. 390
Summary	p. 397
User Relevance Feedback and Collaborative Filtering	p. 398
Challenges in Interpreting the User Feedback	p. 400
Alternative Ways of Using the Collected Feedback in Query Processing	p. 401
Query Rewriting in Vector Space Models	p. 404
Relevance Feedback in Probabilistic Models	p. 404
Relevance Feedback in Probabilistic Language Modeling	p. 408
Pseudorelevance Feedback	p. 411
Feedback Decay	p. 411
Collaborative Filtering	p. 413
Summary	p. 425
Bibliography	p. 427
Index	p. 473
Table of Contents provided by Ingram. All Rights Reserved.

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