InformationTheory in Computer Visionand Pattern Recognition 2009th Edition by Francisco Escolano, Pablo Suau, Boyan Bonev ISBN 1848822960 9781848822962

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Product details:

ISBN 10: 1848822960
ISBN 13: 9781848822962
Author: Francisco Escolano, Pablo Suau, Boyan Bonev

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.

InformationTheory in Computer Visionand Pattern Recognition 2009th Table of contents:

• Mathematical Foundations

  • Basic Information Theory Concepts
  • Entropy, Mutual Information, and Kullback-Leibler Divergence
  • Statistical Models and Information Measures
  • Information Theory and Probability Theory
  • Information Theoretic Approaches in Pattern Recognition

• Image Processing and Information Theory

  • Image Representation and Entropy
  • Image Compression and Quantization
  • Entropy-Based Methods in Image Segmentation
  • Statistical Models in Image Denoising

• Information-Theoretic Features for Computer Vision

  • Feature Selection and Dimensionality Reduction
  • Information-Theoretic Measures in Feature Extraction
  • Mutual Information and Feature Relevance

• Pattern Recognition: Theory and Algorithms

  • Decision Trees and Information Gain
  • Information Theoretic Approaches to Classification
  • Information Theoretic Models in Clustering
  • Generative and Discriminative Models in Pattern Recognition

• Applications of Information Theory in Vision

  • Object Recognition and Classification
  • Visual Tracking and Information Maximization
  • Scene Understanding and Information Flow
  • Action Recognition in Video

• Machine Learning and Information Theory

  • Learning Algorithms and Information Measures
  • Information-Theoretic Approaches to Supervised Learning
  • Semi-Supervised Learning with Information Theory
  • Information Flow in Neural Networks

• Compression and Encoding in Vision

  • Image Compression Algorithms
  • Huffman Coding and Lossless Compression
  • Wavelet-Based Methods
  • Rate-Distortion Theory in Visual Data Compression

• Evaluation Metrics and Information Theoretic Measures

  • Accuracy and Precision in Pattern Recognition
  • Entropy-Based Metrics for Image Quality Assessment
  • Cross-Validation and Information Gain in Model Evaluation

• Challenges and Future Directions

  • Open Problems in Computer Vision and Pattern Recognition
  • The Role of Information Theory in Deep Learning
  • Future Trends in Visual Information Processing

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