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 modelling 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
- Includes supplementary material: sn.pub/extras
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 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.
Reviews
“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)
Authors and Affiliations
About the author
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."
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/10.1007/978-3-030-17989-2
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Nature Switzerland AG 2019
Softcover ISBN: 978-3-030-17991-5Published: 14 August 2020
eBook ISBN: 978-3-030-17989-2Published: 13 May 2019
Series ISSN: 1868-0941
Series E-ISSN: 1868-095X
Edition Number: 1
Number of Pages: XXXI, 314
Number of Illustrations: 85 b/w illustrations, 117 illustrations in colour
Topics: Image Processing and Computer Vision, Data Mining and Knowledge Discovery, Machine Learning, Engineering Mathematics, Big Data