| 書目名稱 | Fundamentals of Image Data Mining | | 副標(biāo)題 | Analysis, Features, | | 編輯 | Dengsheng Zhang | | 視頻video | http://file.papertrans.cn/351/350338/350338.mp4 | | 概述 | Presents a complete introduction to image data mining, and a treasure trove of cutting-edge techniques in image data mining.Describes the applied mathematics and mathematical modeling in an engaging s | | 叢書名稱 | Texts in Computer Science | | 圖書封面 |  | | 描述 | .This 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 techniqu | | 出版日期 | Textbook 2021Latest edition | | 關(guān)鍵詞 | Image Analysis; Feature Extraction; Machine Learning; Image Retrieval; Wavelet Transforms; Support Vector | | 版次 | 2 | | doi | https://doi.org/10.1007/978-3-030-69251-3 | | isbn_softcover | 978-3-030-69253-7 | | isbn_ebook | 978-3-030-69251-3Series ISSN 1868-0941 Series E-ISSN 1868-095X | | issn_series | 1868-0941 | | copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
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