Overview
- 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 style, complete with an accessible introduction to the foundational and engineering mathematics
- Offers a shortcut entry into AI and machine learning, introducing four major machine learning tools with gentle mathematics
Part of the book series: Texts in Computer Science (TCS)
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Table of contents (13 chapters)
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Front Matter
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Preliminaries
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Front Matter
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Image Representation and Feature Extraction
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Front Matter
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Image Classification and Annotation
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Front Matter
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Image Retrieval and Presentation
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Front Matter
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Back Matter
Keywords
About this book
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.
Authors and Affiliations
About the author
Dr. Dengsheng Zhang is Senior Lecturer in the School of Engineering, Information Technology and Physical Sciences at Federation University Australia and a Guest Professor of Xi'an University of Posts & Telecommunications, China. He is on the list of Top 2% Scientists in the World ranked by Stanford University. Dr Zhang was the Textbook & Academic Authors Association’s winner of their 2020 Most Promising New Textbook Award, with the judges noting:
“Fundamentals of Image Data Mining provides excellent coverage of current algorithms and techniques in image analysis. It does this using a progression of essential and novel image processing tools that give students an in-depth understanding of how the tools fit together and how to apply them to problems.”
Bibliographic Information
Book Title: Fundamentals of Image Data Mining
Book Subtitle: Analysis, Features, Classification and Retrieval
Authors: Dengsheng Zhang
Series Title: Texts in Computer Science
DOI: https://doi.org/10.1007/978-3-030-69251-3
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021
Hardcover ISBN: 978-3-030-69250-6Published: 26 June 2021
Softcover ISBN: 978-3-030-69253-7Published: 27 June 2022
eBook ISBN: 978-3-030-69251-3Published: 25 June 2021
Series ISSN: 1868-0941
Series E-ISSN: 1868-095X
Edition Number: 2
Number of Pages: XXXIII, 363
Number of Illustrations: 112 b/w illustrations, 131 illustrations in colour
Topics: Computer Science, general