000			03138nam a22005415i 4500
001			978-3-662-45000-0
003			DE-He213
005			20200421112227.0
007			cr nn 008mamaa
008			150105s2014 gw | s |||| 0|eng d
020			_a9783662450000 _9978-3-662-45000-0
024	7		_a10.1007/978-3-662-45000-0 _2doi
050		4	_aQ337.5
050		4	_aTK7882.P3
072		7	_aUYQP _2bicssc
072		7	_aCOM016000 _2bisacsh
082	0	4	_a006.4 _223
100	1		_aHuang, Yongzhen. _eauthor.
245	1	0	_aFeature Coding for Image Representation and Recognition _h[electronic resource] / _cby Yongzhen Huang, Tieniu Tan.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg : _bImprint: Springer, _c2014.
300			_aXIII, 74 p. 36 illus., 32 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aSpringerBriefs in Computer Science, _x2191-5768
505	0		_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.
520			_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.
650		0	_aComputer science.
650		0	_aAlgorithms.
650		0	_aArtificial intelligence.
650		0	_aImage processing.
650		0	_aPattern recognition.
650	1	4	_aComputer Science.
650	2	4	_aPattern Recognition.
650	2	4	_aImage Processing and Computer Vision.
650	2	4	_aArtificial Intelligence (incl. Robotics).
650	2	4	_aAlgorithm Analysis and Problem Complexity.
700	1		_aTan, Tieniu. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783662449998
830		0	_aSpringerBriefs in Computer Science, _x2191-5768
856	4	0	_uhttp://dx.doi.org/10.1007/978-3-662-45000-0
912			_aZDB-2-SCS
942			_cEBK
999			_c57727 _d57727