EAN: 9781848822962

INFORMATION THEORY IN COMPUTER VISION AND PATTE...

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 a cross-fertilization of both areas.

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