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

• Κύριος συγγραφέας: Chang, Edward Y. (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg, 2011.
• Θέματα: Computer science.; Data mining.; Multimedia information systems.; Image processing.; Machinery.; Computer Science.; Image Processing and Computer Vision.; Machinery and Machine Elements.; Data Mining and Knowledge Discovery.; Multimedia Information Systems.

 "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).