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008			210625s2021 sz | o |||| 0|eng d
020			_a9783030692513
024	7		_a10.1007/978-3-030-69251-3 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9 .D343 _b2021 EB
100	1		_aZhang, Dengsheng _9681241
245	1	0	_aFundamentals of Image Data Mining : _bAnalysis, Features, Classification and Retrieval _cby Dengsheng Zhang
250			_aSecond edition 2021
264		1	_aCham _bSpringer International Publising _c2021
300			_a1 recurso en línea (XXXIII, 363 páginas) _b243 ilustraciones, 131 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aarchivo de texto _bPDF
490	0		_aTexts in Computer Science _x1868-095X
490	0		_aComputer Science (SpringerNature-11645)
490	0		_aComputer Science (R0) (SpringerNature-43710)
505	0		_a1. Fourier Transform -- 2. Windowed Fourier Transform -- 3. Wavelet Transform -- 4. Color Feature Extraction -- 5. Texture Feature Extraction -- 6. Shape Representation -- 7. Bayesian Classification -- Support Vector Machines -- 8. Artificial Neural Networks -- 9. Image Annotation with Decision Trees.-10. Image Indexing -- 11. Image Ranking -- 12. Image Presentation -- 13. Appendix.
520	3		_aThis unique and useful textbook presents a comprehensive review of the essentials of image data mining, and the latest cutting-edge techniques used in the field. The coverage spans all aspects of image analysis and understanding, offering deep insights into areas of feature extraction, machine learning, and image retrieval. The theoretical coverage is supported by practical mathematical models and algorithms, utilizing data from real-world examples and experiments. Topics and features: Describes essential tools for image mining, covering Fourier transforms, Gabor filters, and contemporary wavelet transforms Develops many new exercises (most with MATLAB code and instructions) Includes review summaries at the end of each chapter Analyses state-of-the-art models, algorithms, and procedures for image mining Integrates new sections on pre-processing, discrete cosine transform, and statistical inference and testing Demonstrates how features like color, texture, and shape can be mined or extracted for image representation Applies powerful classification approaches: Bayesian classification, support vector machines, neural networks, and decision trees Implements imaging techniques for indexing, ranking, and presentation, as well as database visualization This easy-to-follow, award-winning book illuminates how concepts from fundamental and advanced mathematics can be applied to solve a broad range of image data mining problems encountered by students and researchers of computer science. Students of mathematics and other scientific disciplines will also benefit from the applications and solutions described in the text, together with the hands-on exercises that enable the reader to gain first-hand experience of computing.
988			_aSpringer_Computer_2021
650		7	_2embne _9162648 _aData mining
776	0	8	_iPrinted edition: _z9783030692506
776	0	8	_iPrinted edition: _z9783030692520
776	0	8	_iPrinted edition: _z9783030692537
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-69251-3 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b01/2022 _dz _eh _zSI