Fundamentals of image data mining : analysis, features, classification and retrieval / Dengsheng Zhang.
2021
QA76.9.D343 Z43 2021
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Details
Title
Fundamentals of image data mining : analysis, features, classification and retrieval / Dengsheng Zhang.
Author
Edition
Second edition.
ISBN
9783030692513 (electronic bk.)
3030692515 (electronic bk.)
9783030692506
3030692507
3030692515 (electronic bk.)
9783030692506
3030692507
Published
Cham : Springer, [2021]
Copyright
©2021
Language
English
Description
1 online resource : illustrations (some color)
Item Number
10.1007/978-3-030-69251-3 doi
Call Number
QA76.9.D343 Z43 2021
Dewey Decimal Classification
006.3/12
Summary
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.
Bibliography, etc. Note
Includes bibliographical references and index.
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Access limited to authorized users.
Digital File Characteristics
text file
PDF
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed July 1, 2021).
Series
Texts in computer science, 1868-0941
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Table of Contents
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.
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.