NDLI: Efficient discriminative learning of parts-based models

Content Provider	IEEE Xplore Digital Library
Author	Kumar, M.P. Zisserman, A. Torr, P.H.S.
Copyright Year	2009
Description	Author affiliation: Dept. of Computing, Oxford Brookes University, UK (Torr, P.H.S.) \|\| Dept. of Engineering Science, University of Oxford, UK (Zisserman, A.) \|\| Computer Science Dept., Stanford University, USA (Kumar, M.P.)
Abstract	Supervised learning of a parts-based model can be formulated as an optimization problem with a large (exponential in the number of parts) set of constraints. We show how this seemingly difficult problem can be solved by (i) reducing it to an equivalent convex problem with a small, polynomial number of constraints (taking advantage of the fact that the model is tree-structured and the potentials have a special form); and (ii) obtaining the globally optimal model using an efficient dual decomposition strategy. Each component of the dual decomposition is solved by a modified version of the highly optimized SVM-Light algorithm. To demonstrate the effectiveness of our approach, we learn human upper body models using two challenging, publicly available datasets. Our model accounts for the articulation of humans as well as the occlusion of parts. We compare our method with a baseline iterative strategy as well as a state of the art algorithm and show significant efficiency improvements.
Starting Page	552
Ending Page	559
File Size	1185426
Page Count	8
File Format	PDF
ISBN	9781424444205
ISSN	15505499
DOI	10.1109/ICCV.2009.5459192
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-29
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Layout Lighting Least squares methods Light sources Least squares approximation Automation Educational institutions Information science Geometry Jacobian matrices
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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