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Description
(Text)
This reader-friendly 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 the essential tools for image mining, covering Fourier transforms, Gabor filters, and contemporary wavelet transforms; reviews a varied range of state-of-the-art models, algorithms, and procedures for image mining; emphasizes how to deal with real image data for practical image mining; highlights how such features as color, texture, and shape can be mined or extracted from images for image representation; presents four powerful approaches for classifying image data, namely, Bayesian classification, Support Vector Machines, Neural Networks, and Decision Trees; discusses techniques for indexing, image ranking, and image presentation, along with image database visualization methods; provides self-test exercises with instructions or Matlab code, as well as review summaries at the end of each chapter.
This easy-to-follow work 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.
(Table of content)
Part I: Preliminaries
Fourier Transform
Windowed Fourier Transform
Wavelet Transform
Part II: Image Representation and Feature Extraction
Color Feature Extraction
Texture Feature Extraction
Shape Representation
Part III: Image Classification and Annotation
Bayesian Classification
Support Vector Machines
Artificial Neural Networks
Image Annotation with Decision Trees
Part IV: Image Retrieval and Presentation
Image Indexing
Image Ranking
Image Presentation
Appendix: Deriving the Conditional Probability of a Gaussian Process
(Review)
"The book is clearly written and the chapters follow a logical order. Almost all the figures are in color, which adds extra value to the explanation. ... the book should be useful to anyone interested in mining image data and would certainly be a valuable addition to their personal library." (Hector Antonio Villa-Martinez, Computing Reviews, September 21, 2020)
(Author portrait)
Dr. Dengsheng Zhang is a Senior Lecturer in the School of Science, Engineering and Information Technology at Federation University Australia.
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Textbook & Academic Authors Association 2020 Most Promising New Textbook Award Winner!
The judges said:
"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."
