Fundamentals of image data mining : analysis, features, classification and retrieval
著者
書誌事項
Fundamentals of image data mining : analysis, features, classification and retrieval
（Texts in computer science）
Springer, c2021
2nd ed
- : hardback
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注記
Includes bibliographical references and index
内容説明・目次
内容説明
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.
目次
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.
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