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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.

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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.

정보제공 :

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