Fundamentals of Image Data Mining / Texts in Computer Science (PDF)

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

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