Structural Information Theory
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Structural Information Theory
Author: Emanuel Leeuwenberg
language: en
Publisher: Cambridge University Press
Release Date: 2013
A coherent and comprehensive theory of visual pattern classification with quantitative models, verifiable predictions and extensive empirical evidence.
Information Theory
Information theory always has the dual appeal of bringing important concepts to the study of communication in society, and of providing a calculus for information flows within systems. This book introduces readers to basic concepts of information theory, extending its original linear conception of communication to many variables, networks, and higher-order interactions (including loops) and developing it into a method for analyzing qualitative data. It elaborates on the algebra of entropy and information, shows how complex models of data are constructed and tested, describes algorithms for exploring multivariate structures using such models, and gives illustrative applications of these techniques. The book is designed as a text but it can also serve as a handbook for social researchers and systems theorists with an interest in communication.
Information Theory in Computer Vision and Pattern Recognition
Author: Francisco Escolano Ruiz
language: en
Publisher: Springer Science & Business Media
Release Date: 2009-07-14
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.