Foundations of Large-Scale Multimedia Information Management and Retrieval: Mathematics of Perception

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"Foundations of Large-Scale Multimedia Information Management and Retrieval: Mathematics of Perception"covers knowledge representation and semantic analysis of multimedia data and scalability in signal extraction, data mining, and indexing. The book is divided into two parts: Part I - Knowledge Representation and Semantic Analysis focuses on the key components of mathematics of perception as it applies to data management and retrieval. These include feature selection/reduction, knowledge representation, semantic analysis, distance function formulation for measuring similarity, and multimodal fusion. Part II - Scalability Issues presents indexing and distributed methods for scaling up these components for high-dimensional data and Web-scale datasets. The book presents some real-world applications and remarks on future research and development directions.

The book is designed for researchers, graduate students, and practitioners in the fields of Computer Vision, Machine Learning, Large-scale Data Mining, Database, and Multimedia Information Retrieval.

Dr. Edward Y. Chang was a professor at the Department of Electrical & Computer Engineering, University of California at Santa Barbara, before he joined Google as a research director in 2006. Dr. Chang received his M.S. degree in Computer Science and Ph.D degree in Electrical Engineering, both from Stanford University.

Author(s): Edward Y. Chang (auth.)
Edition: 1
Publisher: Springer-Verlag Berlin Heidelberg
Year: 2011

Language: English
Pages: 291
Tags: Image Processing and Computer Vision; Machinery and Machine Elements; Data Mining and Knowledge Discovery; Multimedia Information Systems

Front Matter....Pages i-xviii
Introduction: Key Subroutines of Multimedia Data Management....Pages 1-11
Perceptual Feature Extraction....Pages 13-35
Query Concept Learning....Pages 37-72
Similarity....Pages 73-95
Formulating Distance Functions....Pages 97-119
Multimodal Fusion....Pages 121-140
Fusing Content and Context with Causality....Pages 141-169
Combinational Collaborative Filtering, Considering Personalization....Pages 171-190
Imbalanced Data Learning....Pages 191-211
PSVM: Parallelizing Support Vector Machines on Distributed Computers....Pages 213-230
Approximate High-Dimensional Indexing with Kernel....Pages 231-258
Speeding Up Latent Dirichlet allocation with Parallelization and Pipeline Strategies....Pages 259-286
Back Matter....Pages 287-291