000 | 04503nam a22005895i 4500 | ||
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001 | 978-3-030-17989-2 | ||
003 | DE-He213 | ||
005 | 20240423130107.0 | ||
007 | cr nn 008mamaa | ||
008 | 190513s2019 sz | s |||| 0|eng d | ||
020 |
_a9783030179892 _9978-3-030-17989-2 |
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024 | 7 |
_a10.1007/978-3-030-17989-2 _2doi |
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050 | 4 | _aTA1634 | |
072 | 7 |
_aUYQV _2bicssc |
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_aCOM016000 _2bisacsh |
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_aUYQV _2thema |
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082 | 0 | 4 |
_a006.37 _223 |
100 | 1 |
_aZhang, Dengsheng. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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245 | 1 | 0 |
_aFundamentals of Image Data Mining _h[electronic resource] : _bAnalysis, Features, Classification and Retrieval / _cby Dengsheng Zhang. |
250 | _a1st ed. 2019. | ||
264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2019. |
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300 |
_aXXXI, 314 p. 202 illus., 117 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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490 | 1 |
_aTexts in Computer Science, _x1868-095X |
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505 | 0 | _aPart I: Preliminaries -- Fourier Transform -- Windowed Fourier Transform -- Wavelet Transform -- Part II: Image Representation and Feature Extraction -- Color Feature Extraction -- Texture Feature Extraction -- Shape Representation -- Part III: Image Classification and Annotation -- Bayesian Classification -- Support Vector Machines -- Artificial Neural Networks -- Image Annotation with Decision Trees -- Part IV: Image Retrieval and Presentation -- Image Indexing -- Image Ranking -- Image Presentation -- Appendix: Deriving the Conditional Probability of a Gaussian Process. | |
520 | _aThis reader-friendly 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 the essential tools for image mining, covering Fourier transforms, Gabor filters, and contemporary wavelet transforms Reviews a varied range of state-of-the-art models, algorithms, and procedures for image mining Emphasizes how to deal with real image data for practical image mining Highlights how such features as color, texture, and shape can be mined or extracted from images for image representation Presents four powerful approaches for classifying image data, namely, Bayesian classification, Support Vector Machines, Neural Networks, and Decision Trees Discusses techniques for indexing, image ranking, and image presentation, along with image database visualization methods Provides self-test exercises with instructions or Matlab code, as well as review summaries at the end of each chapter This easy-to-follow work 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. Dr. Dengsheng Zhang is a Senior Lecturer in the School of Science, Engineering and Information Technology at Federation University Australia. | ||
650 | 0 | _aComputer vision. | |
650 | 0 | _aData mining. | |
650 | 0 | _aMachine learning. | |
650 | 0 | _aEngineering mathematics. | |
650 | 0 | _aBig data. | |
650 | 1 | 4 | _aComputer Vision. |
650 | 2 | 4 | _aData Mining and Knowledge Discovery. |
650 | 2 | 4 | _aMachine Learning. |
650 | 2 | 4 | _aEngineering Mathematics. |
650 | 2 | 4 | _aBig Data. |
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783030179885 |
776 | 0 | 8 |
_iPrinted edition: _z9783030179908 |
776 | 0 | 8 |
_iPrinted edition: _z9783030179915 |
830 | 0 |
_aTexts in Computer Science, _x1868-095X |
|
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-030-17989-2 |
912 | _aZDB-2-SCS | ||
912 | _aZDB-2-SXCS | ||
942 | _cSPRINGER | ||
999 |
_c184963 _d184963 |