000 03778nam a22005295i 4500
001 978-3-642-20429-6
003 DE-He213
005 20140220083801.0
007 cr nn 008mamaa
008 110825s2011 gw | s |||| 0|eng d
020 _a9783642204296
_9978-3-642-20429-6
024 7 _a10.1007/978-3-642-20429-6
_2doi
050 4 _aTA1637-1638
050 4 _aTA1637-1638
072 7 _aUYT
_2bicssc
072 7 _aUYQV
_2bicssc
072 7 _aCOM012000
_2bisacsh
072 7 _aCOM016000
_2bisacsh
082 0 4 _a006.6
_223
082 0 4 _a006.37
_223
100 1 _aChang, Edward Y.
_eauthor.
245 1 0 _aFoundations of Large-Scale Multimedia Information Management and Retrieval
_h[electronic resource] :
_bMathematics of Perception /
_cby Edward Y. Chang.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2011.
300 _aXVIII, 291p. 108 illus., 60 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aPart 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).
520 _a"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.
650 0 _aComputer science.
650 0 _aData mining.
650 0 _aMultimedia systems.
650 0 _aComputer vision.
650 0 _aEngineering.
650 1 4 _aComputer Science.
650 2 4 _aImage Processing and Computer Vision.
650 2 4 _aMachinery and Machine Elements.
650 2 4 _aData Mining and Knowledge Discovery.
650 2 4 _aMultimedia Information Systems.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642204289
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-20429-6
912 _aZDB-2-SCS
999 _c107785
_d107785