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010   $a3-662-45000-3
024 7 $a10.1007/978-3-662-45000-0
035   $a(CKB)3710000000332321
035   $a(EBL)1966908
035   $a(OCoLC)908086378
035   $a(SSID)ssj0001424475
035   $a(PQKBManifestationID)11778068
035   $a(PQKBTitleCode)TC0001424475
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035   $a(MiAaPQ)EBC1966908
035   $a(DE-He213)978-3-662-45000-0
035   $a(PPN)183518160
035   $a(EXLCZ)993710000000332321
100   $a20150105d2014      u|         0
101 0 $aeng
135   $aur|n|---|||||
181   $ctxt
182   $cc
183   $acr
200 10$aFeature coding for image representation and recognition /$fby Yongzhen Huang, Tieniu Tan
205   $a1st ed. 2014.
210  1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2014.
215   $a1 online resource (80 p.)
225 1 $aSpringerBriefs in Computer Science,$x2191-5768
300   $aDescription based upon print version of record.
311   $a3-662-44999-4 
320   $aIncludes bibliographical references.
327   $a1. Introduction -- 2. Taxonomy -- 3. Representative Feature Coding Algorithms -- 4. Evolution of Feature Coding -- 5. Experimental Study of Feature Coding -- 6. Enhancement via Integrating Spatial Information -- 7. Enhancement via Integrating High Order Coding Information -- 8. Conclusion.
330   $aThis brief presents a comprehensive introduction to feature coding, which serves as a key module for the typical object recognition pipeline. The text offers a rich blend of theory and practice while reflects the recent developments on feature coding, covering the following five aspects: (1) Review the state-of-the-art, analyzing the motivations and mathematical representations of various feature coding methods; (2) Explore how various feature coding algorithms evolve along years; (3) Summarize the main characteristics of typical feature coding algorithms and categorize them accordingly; (4) Discuss the applications of feature coding in different visual tasks, analyze the influence of some key factors in feature coding with intensive experimental studies; (5) Provide the suggestions of how to apply different feature coding methods and forecast the potential directions for future work on the topic. It is suitable for students, researchers, practitioners interested in object recognition.
410  0$aSpringerBriefs in Computer Science,$x2191-5768
606   $aPattern recognition
606   $aOptical data processing
606   $aArtificial intelligence
606   $aAlgorithms
606   $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X
606   $aImage Processing and Computer Vision$3https://scigraph.springernature.com/ontologies/product-market-codes/I22021
606   $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000
606   $aAlgorithm Analysis and Problem Complexity$3https://scigraph.springernature.com/ontologies/product-market-codes/I16021
615  0$aPattern recognition.
615  0$aOptical data processing.
615  0$aArtificial intelligence.
615  0$aAlgorithms.
615 14$aPattern Recognition.
615 24$aImage Processing and Computer Vision.
615 24$aArtificial Intelligence.
615 24$aAlgorithm Analysis and Problem Complexity.
676   $a004
676   $a005.1
676   $a006.3
676   $a006.37
700   $aHuang$b Yongzhen$4aut$4http://id.loc.gov/vocabulary/relators/aut$0918911
702   $aTan$b Tieniu$4aut$4http://id.loc.gov/vocabulary/relators/aut
906   $aBOOK
912   $a9910298970103321
996   $aFeature Coding for Image Representation and Recognition$92060905
997   $aUNINA