Machine Learning in Computer Vision

N. Sebe, Ira Cohen, Ashutosh Garg, Thomas S. Huang (auth.)1402032749, 9781402032745

The goal of this book is to address the use of several important machine learning techniques into computer vision applications. An innovative combination of computer vision and machine learning techniques has the promise of advancing the field of computer vision, which contributes to better understanding of complex real-world applications. The effective usage of machine learning technology in real-world computer vision problems requires understanding the domain of application, abstraction of a learning problem from a given computer vision task, and the selection of appropriate representations for the learnable (input) and learned (internal) entities of the system.In this book, we address all these important aspects from a new perspective: that the key element in the current computer revolution is the use of machine learning to capture the variations in visual appearance, rather than having the designer of the model accomplish this. As a bonus, models learned from large datasets are likely to be more robust and more realistic than the brittle all-design models. This book is intended for computer vision, machine learning, and pattern recognition researchers as well as for graduate students in computer science and electrical engineering.

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Table of contents :
Introduction….Pages 1-13
Theory: Probabilistic Classifiers….Pages 15-43
Theory: Generalization Bounds….Pages 45-64
Theory: Semi-Supervised Learning….Pages 65-101
Algorithm: Maximum Likelihood Minimum Entropy HMM….Pages 103-118
Algorithm: Margin Distribution Optimization….Pages 119-128
Algorithm: Learning the Structure of Bayesian Network Classifiers….Pages 129-156
Application: Office Activity Recognition….Pages 157-173
Application: Multimodal Event Detection….Pages 175-186
Application: Facial Expression Recognition….Pages 187-209
Application: Bayesian Network Classifiers for Face Detection….Pages 211-224