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008			170420s2017 sz ob 001 0 eng d
020			_a3319520814 _q(electronic bk.)
020			_a9783319520810 _q(electronic bk.)
020			_z3319520806
020			_z9783319520803 _q(print)
040			_aN$T _cN$T _dEBLCP _dN$T _dGW5XE _dOCLCF _dYDX _dUAB _dESU _dAZU _dUPM _dCOO _dOTZ _dVT2 _dOCLCQ _dIOG _dU3W _dES-MaUEC _bspa
050		4	_aZA3075 _b.R434 2017 EB
245	0	0	_aRecent advances in intelligent image search and video retrieval _cChengjun Liu, editor.
264		1	_aCham, Switzerland _bSpringer _c2017.
300			_a1 recurso en línea
336			_aTexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	0		_aIntelligent systems reference library _vvolume 121
500			_aSpringerLink _bSpringer Engineering eBooks 2017 English+International
504			_aIncluye referencias bibliográficas e índice
505	0		_aPreface; Contents; Contributors; Acronyms; 1 Feature Representation and Extraction for Image Search and Video Retrieval; 1.1 Introduction; 1.2 Spatial Pyramid Matching, Soft Assignment Coding, Fisher Vector Coding, and Sparse Coding; 1.2.1 Spatial Pyramid Matching; 1.2.2 Soft Assignment Coding; 1.2.3 Fisher Vector Coding; 1.2.4 Sparse Coding; 1.2.5 Some Sparse Coding Variants; 1.3 Local Binary Patterns (LBP), Feature LBP (FLBP), Local Quaternary Patterns (LQP), and Feature LQP (FLQP); 1.4 Scale Invariant Feature Transform (SIFT) and SIFT Variants; 1.4.1 Color SIFT; 1.4.2 SURF; 1.4.3 MSIFT.
505	8		_a1.4.4 DSP-SIFT1.4.5 LPSIFT; 1.4.6 FAIR-SURF; 1.4.7 Laplacian SIFT; 1.4.8 Edge-SIFT; 1.4.9 CSIFT; 1.4.10 RootSIFT; 1.4.11 PCA-SIFT; 1.5 Conclusion; References; 2 Learning and Recognition Methods for Image Search and Video Retrieval; 2.1 Introduction; 2.2 Deep Learning Networks and Models; 2.2.1 Feedforward Deep Neural Networks; 2.2.2 Deep Autoencoders; 2.2.3 Convolutional Neural Networks (CNNs); 2.2.4 Deep Boltzmann Machine (DBM); 2.3 Support Vector Machines; 2.3.1 Linear Support Vector Machine; 2.3.2 Soft-Margin Support Vector Machine; 2.3.3 Non-linear Support Vector Machine.
505	8		_a2.3.4 Simplified Support Vector Machines2.3.5 Efficient Support Vector Machine; 2.3.6 Applications of SVM; 2.4 Other Popular Kernel Methods and Similarity Measures; 2.5 Conclusion; References; 3 Improved Soft Assignment Coding for Image Classification; 3.1 Introduction; 3.2 Related Work; 3.3 The Improved Soft-Assignment Coding; 3.3.1 Revisiting the Soft-Assignment Coding; 3.3.2 Introduction to Fisher Vector and VLAD Method; 3.3.3 The Thresholding Normalized Visual Word Plausibility; 3.3.4 The Power Transformation; 3.3.5 Relation to VLAD Method; 3.4 Experiments.
505	8		_a3.4.1 The UIUC Sports Event Dataset3.4.2 The Scene 15 Dataset; 3.4.3 The Caltech 101 Dataset; 3.4.4 The Caltech 256 Dataset; 3.4.5 In-depth Analysis; 3.5 Conclusion; References; 4 Inheritable Color Space (InCS) and Generalized InCS Framework with Applications to Kinship Verification; 4.1 Introduction; 4.2 Related Work; 4.3 A Novel Inheritable Color Space (InCS); 4.4 Properties of the InCS; 4.4.1 The Decorrelation Property; 4.4.2 Robustness to Illumination Variations; 4.5 The Generalized InCS (GInCS) Framework; 4.6 Experiments.
505	8		_a4.6.1 Experimental Results Using the KinFaceW-I and the KinFaceW-II Datasets4.6.2 Experimental Results Using the UB KinFace Dataset; 4.6.3 Experimental Results Using the Cornell KinFace Dataset; 4.7 Comprehensive Analysis; 4.7.1 Comparative Evaluation of the InCS and Other Color Spaces; 4.7.2 The Decorrelation Property of the InCS Method; 4.7.3 The Robustness of the InCS and the GInCS to Illumination Variations; 4.7.4 Performance of Different Color Components of the InCS and the GInCS; 4.7.5 Comparison Between the InCS and the Generalized InCS.
520	3		_aThis book initially reviews the major feature representation and extraction methods and effective learning and recognition approaches, which have broad applications in the context of intelligent image search and video retrieval. It subsequently presents novel methods, such as improved soft assignment coding, Inheritable Color Space (InCS) and the Generalized InCS framework, the sparse kernel manifold learner method, the efficient Support Vector Machine (eSVM), and the Scale-Invariant Feature Transform (SIFT) features in multiple color spaces. Lastly, the book presents clothing analysis for subject identification and retrieval, and performance evaluation methods of video analytics for traffic monitoring. Digital images and videos are proliferating at an amazing speed in the fields of science, engineering and technology, media and entertainment. With the huge accumulation of such data, keyword searches and manual annotation schemes may no longer be able to meet the practical demand for retrieving relevant content from images and videos, a challenge this book addresses.
650		7	_aInteligencia artificial _2embne _0(OCoLC)fst00817247 _0 _9413115
700	1		_aLiu, Chengjun _c(Computer scientist), _eeditor literario
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=http://link.springer.com/10.1007/978-3-319-52081-0 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
988			_aEBOOK, asignarmaterias, EBSPRINGER_2017C
998			_b02/2018 _dz _e- _zSI
999			_c95834 _d95834 _x1