Combinatorial and statistical methods for part selection for object recognition

Authors:
Zhipeng Zhao;Akshay Vashist;Ahmed Elgammal;Ilya Muchnik;Casimir Kulikowski
Affiliations:
Department of Computer Science, Rutgers, The State University of New Jersey, Piscataway, NJ, USA;Department of Computer Science, Rutgers, The State University of New Jersey, Piscataway, NJ, USA;Department of Computer Science, Rutgers, The State University of New Jersey, Piscataway, NJ, USA;Department of Computer Science, Rutgers, The State University of New Jersey, Piscataway, NJ, USA,Centre for Discrete Mathematics and Theoretical Computer Science (DIMACS), Rutgers, The State Unive ...;Department of Computer Science, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
Venue:
International Journal of Computer Mathematics - Computer Vision and Pattern Recognition
Year:
2007

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Abstract

In object recognition tasks, where images are represented as constellations of image patches, often many patches correspond to the cluttered background. In this paper, we present a two-stage method for selecting the image patches which characterize the target object class and are capable of discriminating between the positive images containing the target objects and the complementary negative images. The first stage uses a combinatorial optimization formulation on a weighted multipartite graph. The following stage is a statistical method for selecting discriminative patches from the positive images. Another contribution of this paper is the part-based probabilistic method for object recognition, which uses a common reference frame instead of reference patch to avoid possible occlusion problems. We also explore different feature representation using principal component analysis (PCA) and 2D PCA. The experiment demonstrates our approach has outperformed most of the other known methods on a popular benchmark dataset while approaching the best known results.