This unique and useful textbook presents a comprehensive review of the essentials of image data mining and the latest cuttingedge 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 realworld examples and experiments.ampampnbspampampnbspTopics and featuresampampnbspampampnbspDescribes essential tools for image mining covering Fourier transforms Gabor filters and contemporary wavelet transformsDevelopsampampnbspmanyampampnbspnew exercisesampampnbspmost with MATLAB code and instructionsIncludes review summaries at the end of each chapterAnalyses stateoftheart models algorithms and procedures for image miningIntegratesampampnbspnewampampnbspsectionsampampnbspon preprocessing discrete cosine transform and statistical inference and testingDemonstrates how features like color texture and shape can be mined or extracted for image representationApplies powerful classification approaches Bayesian classification support vector machines neural networks and decision treesImplements imaging techniques for indexing ranking and presentation as well as database visualizationampampnbspThis easytofollow awardwinning book 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.