Foundations of Large-Scale Multimedia Information Management and Retrieval
Mathematics of Perception
Chang, Edward Y.
Jointly published with Tsinghua University Press
2011, XVIII, 291 p.
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Written by the utmost expert at Google Inc Dr Edward Chang, Director of Research
Covering knowledge representation, semantic analysis, and scalability issues in one comprehensive book
Written for the specialist while maintaining an accessible approach for students alike
A must-have in this ever-faster evolving area
"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.
Part I - Knowledge Representation and Semantic Analysis.- 1. Mathematics of Perception.- 2. Supervised Learning (based on tutorial DASFAA 2003).- 3. Query Concept Learning (based on IEEE TMM 2005).- 4. Feature Extraction.- 5. Feature Reduction (based on MM 04, ICME 05, IPAM).- 6. Similarity (based on MMJ 2002, CIKM 04, ICML 05).- Part II - Scalability Issues.- 7. Imbalanced Data Learning (based on TKDE 2005).- 8. Semantics Fusion (based on MM 04, MM05, KDD 08).- 9. Kernel Machines Speedup (based on SDM 05, KDD 06, NIPS 07).- 10. Kernel Indexing (based on TKDE 06).- 11. Put It All Together (based on SPIE 06).
Distribution rights in China: Tsinghua University Press.