Object detection using spatial histogram features

Authors:
Hongming Zhang;Wen Gao;Xilin Chen;Debin Zhao
Affiliations:
Department of Computer Science and technology, Harbin Institute of Technology, No. 92, west Dazhi street, Harbin 150001, China;Department of Computer Science and technology, Harbin Institute of Technology, No. 92, west Dazhi street, Harbin 150001, China and Institute of Computing Technology, Chinese Academy of Sciences, B ...;Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100080, China;Department of Computer Science and technology, Harbin Institute of Technology, No. 92, west Dazhi street, Harbin 150001, China
Venue:
Image and Vision Computing
Year:
2006

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Abstract

In this paper, we propose an object detection approach using spatial histogram features. As spatial histograms consist of marginal distributions of an image over local patches, they can preserve texture and shape information of an object simultaneously. We employ Fisher criterion and mutual information to measure discriminability and features correlation of spatial histogram features. We further train a hierarchical classifier by combining cascade histogram matching and support vector machine. The cascade histogram matching is trained via automatically selected discriminative features. A forward sequential selection method is presented to construct uncorrelated and discriminative feature sets for support vector machine classification. We evaluate the proposed approach on two different kinds of objects: car and video text. Experimental results show that the proposed approach is efficient and robust in object detection.