Departmental Papers (CIS)

Document Type

Conference Paper

Date of this Version

December 2001

Comments

Copyright 2001 IEEE. Reprinted from Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2001), Volume 1, pages 551-558.
Publisher URL: http://dx.doi.org/10.1109/CVPR.2001.990522

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NOTE: At the time of publication, author Jianbo Shi was affiliated with Carnegie Mellon University. Currently (March 2005), he is a faculty member in the Department of Computer and Information Science at the University of Pennsylvania.

Abstract

We approach the task of object discrimination as that of learning efficient "codes" for each object class in terms of responses to a set of chosen discriminants. We formulate this approach in an energy minimization framework. The "code" is built incrementally by successively constructing discriminants that focus on pairs of training images of objects that are currently hard to classify. The particular discriminants that we use partition the set of objects of interest into two well-separated groups. We find the optimal discriminant as well as partition by formulating an objective criteria that measures the well-separateness of the partition. We derive an iterative solution that alternates between the solutions for two generalized eigenproblems, one for the discriminant parameters and the other for the indicator variables denoting the partition. We show how the optimization can easily be biased to focus on hard to classify pairs, which enables us to choose new discriminants one by one in a sequential manner We validate our approach on a challenging face discrimination task using parts as features and show that it compares favorably with the performance of an eigenspace method.

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Date Posted: 30 April 2005