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.

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

Autorentext

Dr. Dengsheng Zhang is Senior Lecturer in the School of Engineering, Information Technology and Physical Sciences at Federation University Australia and a Guest Professor of Xi'an University of Posts & Telecommunications, China. He is on the list of Top 2% Scientists in the World ranked by Stanford University. Dr Zhang was the Textbook & Academic Authors Association's winner of their 2020 Most Promising New Textbook Award, with the judges noting: "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."

Inhalt

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.