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Information Theory in Computer Vision and Pattern Recognition by Escolano, Francisco; Suau, Pablo; Bonev, Boyán is a scholarly article available to read on EtoBox.

What is Information Theory in Computer Vision and Pattern Recognition about?

Information theory has proved to be effective for solving many computer vision 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 information...), principles (maximum entropy, minimax entropy...) and theories (rate distortion theory, method of types...). 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.

Author

Escolano, Francisco; Suau, Pablo; Bonev, Boyán

Publisher

Springer London Ltd

Published

2009

Language

EN

ISBN

9781447156932