Fundamentals of Image Data Mining

Analysis, Features, Classification and Retrieval

  • Dengsheng¬†Zhang

Part of the Texts in Computer Science book series (TCS)

Table of contents

  1. Front Matter
    Pages i-xxxi
  2. Preliminaries

    1. Front Matter
      Pages 1-1
    2. Dengsheng Zhang
      Pages 3-23
    3. Dengsheng Zhang
      Pages 25-34
    4. Dengsheng Zhang
      Pages 35-44
  3. Image Representation and Feature Extraction

    1. Front Matter
      Pages 45-47
    2. Dengsheng Zhang
      Pages 49-80
    3. Dengsheng Zhang
      Pages 81-111
    4. Dengsheng Zhang
      Pages 113-154
  4. Image Classification and Annotation

    1. Front Matter
      Pages 155-159
    2. Dengsheng Zhang
      Pages 161-178
    3. Dengsheng Zhang
      Pages 179-205
    4. Dengsheng Zhang
      Pages 207-242
    5. Dengsheng Zhang
      Pages 243-259
  5. Image Retrieval and Presentation

    1. Front Matter
      Pages 261-261
    2. Dengsheng Zhang
      Pages 263-270
    3. Dengsheng Zhang
      Pages 271-287
    4. Dengsheng Zhang
      Pages 289-304
  6. Back Matter
    Pages 305-314

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.

Dr. Dengsheng Zhang is a Senior Lecturer in the School of Science, Engineering and Information Technology at Federation University Australia.


Image analysis Feature extraction Machine learning Image retrieval Wavelet transforms Support vector machines Convolutional neural networks Image segmentation Texture features

Authors and affiliations

  • Dengsheng¬†Zhang
    • 1
  1. 1.Federation University AustraliaChurchillAustralia

Bibliographic information

  • DOI
  • Copyright Information Springer Nature Switzerland AG 2019
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-030-17988-5
  • Online ISBN 978-3-030-17989-2
  • Series Print ISSN 1868-0941
  • Series Online ISSN 1868-095X
  • Buy this book on publisher's site
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