Machine Learning in Computer Vision [electronic resource] /
Sebe, Nicu.author.
Cohen, Ira.author.
Garg, Ashutosh.author.
Huang, Thomas S.author.
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
Description based upon print version of record.
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
Includes bibliographical references (p. [223]-236) and index.
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.
Optical data processing.
User interfaces (Computer systems).
Multimedia information systems.
Mathematical statistics.
Computer Imaging, Vision, Pattern Recognition and Graphics.
User Interfaces and Human Computer Interaction.
Multimedia Information Systems.
Probability and Statistics in Computer Science.