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024 7 _a10.1007/978-3-030-69251-3
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100 1 _aZhang, Dengsheng.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aFundamentals of Image Data Mining
_h[electronic resource] :
_bAnalysis, Features, Classification and Retrieval /
_cby Dengsheng Zhang.
250 _a2nd ed. 2021.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2021.
300 _aXXXIII, 363 p. 243 illus., 131 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 _aTexts in Computer Science,
_x1868-095X
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 _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.
650 0 _aComputer science.
650 1 4 _aComputer Science.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030692506
776 0 8 _iPrinted edition:
_z9783030692520
776 0 8 _iPrinted edition:
_z9783030692537
830 0 _aTexts in Computer Science,
_x1868-095X
856 4 0 _uhttps://doi.org/10.1007/978-3-030-69251-3
912 _aZDB-2-SCS
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