Generic object recognition with regional statistical models and layer joint boosting

Authors:
Jun Gao;Zhao Xie;Xindong Wu
Affiliations:
School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, PR China;School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, PR China;School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, PR China and Department of Computer Science, University of Vermont, Burlington, VT 05405, USA
Venue:
Pattern Recognition Letters
Year:
2007

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Abstract

This paper presents novel regional statistical models for extracting object features, and an improved discriminative learning method, called as layer joint boosting, for generic multi-class object detection and categorization in cluttered scenes. Regional statistical properties on intensities are used to find sharing degrees among features in order to recognize generic objects efficiently. Based on boosting for multi-classification, the layer characteristic and two typical weights in sharing-code maps are taken into account to keep the maximum Hamming distance in categories, and heuristic search strategies are provided in the recognition process. Experimental results reveal that, compared with interest point detectors in representation and multi-boost in learning, joint layer boosting with statistical feature extraction can enhance the recognition rate consistently, with a similar detection rate.