Foundations of Large-Scale Multimedia Information Management and Retrieval
Springer Berlin (Verlag)
978-3-642-20428-9 (ISBN)
"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, MachineLearning, 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.
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. Edward Y. 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).
| Erscheint lt. Verlag | 1.8.2011 |
|---|---|
| Zusatzinfo | XVIII, 291 p. |
| Verlagsort | Berlin |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 645 g |
| Themenwelt | Informatik ► Grafik / Design ► Digitale Bildverarbeitung |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Schlagworte | Data Mining • High-Dimensional Indexing • Image Retrieval • Information Retrieval • Informationsmanagement • Knowledge Representation • Large-scale Data Mining • Multimedia • Multimedia Information Retrieval • Scalability issues • Semantic Analysis • TUP |
| ISBN-10 | 3-642-20428-7 / 3642204287 |
| ISBN-13 | 978-3-642-20428-9 / 9783642204289 |
| Zustand | Neuware |
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