Information Theory in Computer Vision and Pattern Recognition

Information Theory (IT) can be highly effective for formulating and designing algorithmic solutions to many problems in Computer Vision and Pattern Recognition (CVPR). This text introduces and explores the measures, principles, theories, and entropy estimators from IT underlying modern CVPR algorithms, providing comprehensive coverage of the subject through an incremental complexity approach. The authors formulate the main CVPR problems and present the most representative algorithms. In addition, they highlight interesting connections between elements of IT when applied to different problems, leading to the development of a basic research roadmap (the ITinCVPR tube). The result is a novel tool, unique in its conception, both for CVPR and IT researchers, which is intended to contribute as much as possible to a cross-fertilization of both areas. Topics and features:

• Introduces contour and region-based image segmentation in computer vision, covering Jensen-Shannon divergence, the maximum entropy principle, the minimum description length (MDL) principle, and discriminative-generative approaches to segmentation
• Explores problems in image and pattern clustering, discussing Gaussian mixtures, information bottleneck, robust information clustering, and IT-based mean-shift, as well as strategies to form clustering ensembles
• Includes a selection of problems at the end of each chapter, to both consolidate what has been learnt and to test the ability of generalizing the concepts discussed
• Investigates the application of IT to interest points, edge detection and grouping in computer vision, including the concept of Shannon’s entropy, Chernoff information and mutual information, Sanov’s theorem, and the theory of types
• Reviews methods of registration, matching and recognition of images and patterns, considering measures