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Fundamentals of Image Data Mining = Analysis, Features, Classification and Retrieval /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Fundamentals of Image Data Mining/ by Dengsheng Zhang.
Reminder of title:	Analysis, Features, Classification and Retrieval /
Author:	Zhang, Dengsheng.
Description:	XXXIII, 363 p. 243 illus., 131 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Computer science. -
Online resource:	https://doi.org/10.1007/978-3-030-69251-3
ISBN:	9783030692513

Fundamentals of Image Data Mining = Analysis, Features, Classification and Retrieval /
Zhang, Dengsheng.

Fundamentals of Image Data MiningAnalysis, Features, Classification and Retrieval /[electronic resource] :by Dengsheng Zhang. - 2nd ed. 2021. - XXXIII, 363 p. 243 illus., 131 illus. in color.online resource. - Texts in Computer Science,1868-095X. - Texts in Computer Science,.

1. 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.

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 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.

ISBN: 9783030692513

Standard No.: 10.1007/978-3-030-69251-3doiSubjects--Topical Terms:

573171
Computer science.

LC Class. No.: QA75.5-76.95

Dewey Class. No.: 004

Fundamentals of Image Data Mining = Analysis, Features, Classification and Retrieval /
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520 $a 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 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.
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