TY  - BOOK
AU  - Chang,Edward Y.
ED  - SpringerLink (Online service)
TI  - Foundations of Large-Scale Multimedia Information Management and Retrieval: Mathematics of Perception 
SN  - 9783642204296
AV  - TA1637-1638 
U1  - 006.6 23
PY  - 2011///
CY  - Berlin, Heidelberg
PB  - Springer Berlin Heidelberg
KW  - Computer science
KW  - Data mining
KW  - Multimedia systems
KW  - Computer vision
KW  - Engineering
KW  - Computer Science
KW  - Image Processing and Computer Vision
KW  - Machinery and Machine Elements
KW  - Data Mining and Knowledge Discovery
KW  - Multimedia Information Systems
N1  - 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)
N2  - "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
UR  - http://148.231.10.114:2048/login?url=http://link.springer.com/book/10.1007/978-3-642-20429-6
ER  -