Toward a unified probabilistic framework for object recognition and segmentation

Authors:
Huei-Ju Chen;Kuang-Chih Lee;Erik Murphy-Chutorian;Jochen Triesch
Affiliations:
Dept. of Cognitive Science, UC San Diego, CA;OJOS Inc.;Dept. of Electrical and Computer Engineering, UC San Diego, CA;Dept. of Cognitive Science, UC San Diego, CA
Venue:
ISVC'05 Proceedings of the First international conference on Advances in Visual Computing
Year:
2005

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Abstract

This paper presents a novel and effective Bayesian belief network that integrates object segmentation and recognition. The network consists of three latent variables that represent the local features, the recognition hypothesis, and the segmentation hypothesis. The probabilities are the result of approximate inference based on stochastic simulations with Gibbs sampling, and can be calculated for large databases of objects. Experimental results demonstrate that this framework outperforms a feed-forward recognition system that ignores the segmentation problem.