Domain Adaptation for Visual Recognition (Foundations and Trends(r) in Computer Graphics and Vision)

Gopalan, Raghuraman; Li, Ruonan; Patel, Vishal M; Chellappa, Rama

ISBN 10: 1680830309 / ISBN 13: 9781680830309

Published by Now Publishers, 2015

About this Item

Seller Inventory # ABLIING23Mar2811580338062

Bibliographic Details

Title: Domain Adaptation for Visual Recognition (...

Publisher: Now Publishers

Publication Date: 2015

Binding: Soft cover

Condition: New

About this title

Synopsis:

Domain adaptation is an active, emerging research area that attempts to address the changes in data distribution across training and testing datasets. With the availability of a multitude of image acquisition sensors, variations due to illumination, and viewpoint among others, computer vision applications present a very natural test bed for evaluating domain adaptation methods. This book provides a comprehensive overview of domain adaptation solutions for visual recognition problems. By starting with the problem description and illustrations, it discusses three adaptation scenarios namely, (i) unsupervised adaptation where the "source domain" training data is partially labeled and the "target domain" test data is unlabeled, (ii) semi-supervised adaptation where the target domain also has partial labels, and (iii) multi-domain heterogeneous adaptation which studies the previous two settings with the source and/or target having more than one domain, and accounts for cases where the features used to represent the data in each domain are different. For all these topics it discusses existing adaptation techniques in the literature, which are motivated by the principles of max-margin discriminative learning, manifold learning, sparse coding, as well as low-rank representations. These techniques have shown improved performance on a variety of applications such as object recognition, face recognition, activity analysis, concept classification, and person detection. The book concludes by analyzing the challenges posed by the realm of "big visual data", in terms of the generalization ability of adaptation algorithms to unconstrained data acquisition as well as issues related to their computational tractability, and draws parallels with the efforts from the vision community on image transformation models, and invariant descriptors so as to facilitate improved understanding of vision problems under uncertainty.

About the Author:

Rama Chellappa is Minta Martin Professor of Engineering and an affiliate Professor of Computer Science at University of Maryland, College Park. He is also affiliated with the Center for Automation Research and UMIACS, and is serving as the Chair of the ECE department. He is a recipient of the K. S. Fu Prize from the IAPR and the Society, Technical Achievement and Meritorious Service Awards from the IEEE Signal Processing Society. He also received the Technical Achievement and Meritorious Service Awards from the IEEE Computer Society. In 2010, he was recognized as an Outstanding ECE by Purdue University. He is a Fellow of the IEEE, IAPR, OSA and AAAS, a Golden Core Member of the IEEE Computer Society, and has served as a Distinguished Lecturer of the IEEE Signal Processing Society as well as the President of the IEEE Biometrics Council.