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.

This volume contains the papers presented at the 7th IAPR-TC-15 Workshop onGraph-BasedRepresentationsinPatternRecognition- GbR2009.Thewo- shop was held in Venice, Italy between May 26-28, 2009. The previous wo- shops in the series were held in Lyon, France (1997), Haindorf, Austria (1999), Ischia, Italy (2001), York, UK (2003), Poitiers, France (2005), and Alicante, Spain (2007). The Technical Committee (TC15, http: //www.greyc.ensicaen.fr/iapr-tc15/) of the IAPR (International Association for Pattern Recognition) was founded in order to federate and to encourage research work at the intersection of pattern recognition and graph theory. Among its activities, the TC15 encourages the organization of special graph sessions in many computer vision conferences and organizes the biennial GbR Workshop. The scienti?c focus of these workshops coversresearchin pattern recognition and image analysis within the graph theory framework. This workshop series traditionally provide a forum for presenting and discussing research results and applications in the intersection of pattern recognition, image analysis and graph theory

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.