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Categorization by learning and combining object parts

Heisele, B; Serre, T; Pontil, M; Vetter, T; Poggio, T; (2002) Categorization by learning and combining object parts. In: Dietterich, TG and Becker, S and Ghahramani, Z, (eds.) ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 14, VOLS 1 AND 2. (pp. 1239 - 1245). M I T PRESS

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Abstract

We describe an algorithm for automatically learning discriminative components of objects with SVM classifiers. It is based on growing image parts by minimizing theoretical bounds on the error probability of an SVM. Component-based face classifiers are then combined in a second stage to yield a hierarchical SVM classifier. Experimental results in face classification show considerable robustness against rotations in depth and suggest performance at significantly better level than other face detection systems. Novel aspects of our approach are: a) an algorithm to learn component-based classification experts and their combination, b) the use of 3-D morphable models for training, and c) a maximum operation on the output of each component classifier which may be relevant for biological models of visual recognition.

Type:Proceedings paper
Title:Categorization by learning and combining object parts
Event:15th Annual Conference on Neural Information Processing Systems (NIPS)
Location:VANCOUVER, CANADA
Dates:2001-12-03 - 2001-12-08
ISBN:0-262-04208-8
UCL classification:UCL > School of BEAMS > Faculty of Engineering Science > Computer Science

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