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Information Theory in Computer Vision and Pattern Recognition

By (author) Francisco Escolano Ruiz, Foreword by Alan L. Yuille, By (author) Pablo Suau PÃ©rez, By (author) BoyÃ¡n Ivanov Bonev

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Information theory has proved to be effective for solving many computer visionÂ and pattern recognition (CVPR) problems (such as image matching, clustering and segmentation, saliency detection, feature selection, optimal classifier design and many others). Nowadays, researchers are widely bringing information theory elements to the CVPR arena. Among these elements there are measures (entropy, mutual informationâ€¦), principles (maximum entropy, minimax entropyâ€¦) and theories (rate distortion theory, method of typesâ€¦). This book explores and introduces the latter elements through an incremental complexity approach at the same time where CVPR problems are formulated and the most representative algorithms are presented. Interesting connections between information theory principles when applied to different problems are highlighted, seeking a comprehensive research roadmap. The result is a novel tool both for CVPR and machine learning researchers, and contributes to across-fertilization of both areas.

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