Probabilistic combination of visual cues for object classification

Authors:
Roman Filipovych;Eraldo Ribeiro
Affiliations:
Computer Vision and Bio-Inspired Computing Laboratory, Department of Computer Sciences, Florida Institute of Technology, Melbourne, FL;Computer Vision and Bio-Inspired Computing Laboratory, Department of Computer Sciences, Florida Institute of Technology, Melbourne, FL
Venue:
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part I
Year:
2007

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Abstract

Recent solutions to object classification have focused on the decomposition of objects into representative parts. However, the vast majority of these methods are based on single visual cue measurements. Psychophysical evidence suggests that humans use multiple visual cues to accomplish recognition. In this paper, we address the problem of integrating multiple visual information for object recognition. Our contribution in this paper is twofold. First, we describe a new probabilistic integration model of multiple visual cues at different spatial locations across the image. Secondly, we use the cue integration framework to classify images of objects by combining two-dimensional and three-dimensional visual cues. Classification results obtained using the method are promising.