Machine Learning in Computer Vision [electronic resource] / by N. Sebe, Ira Cohen, Ashutosh Garg, Thomas S. Huang.

Theory: Probabilistic Classifiers -- Theory: Generalization Bounds -- Theory: Semi-Supervised Learning -- Algorithm: Maximum Likelihood Minimum Entropy HMM -- Algorithm: Margin Distribution Optimization -- Algorithm: Learning the Structure of Bayesian Network Classifiers -- Application: Office Activity Recognition -- Application: Multimodal Event Detection -- Application: Facial Expression Recognition -- Application: Bayesian Network Classifiers for Face Detection.

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.