Object Class Recognition Using SNoW with a Part Vocabulary

Authors:
Ming Wen;Lu Wang;Lei Wang;Qing Zhuo;Wenyuan Wang
Affiliations:
Department of Automation, Tsinghua University, Beijing, 100084, P.R. China;Department of Automation, Tsinghua University, Beijing, 100084, P.R. China;Department of Automation, Tsinghua University, Beijing, 100084, P.R. China;Department of Automation, Tsinghua University, Beijing, 100084, P.R. China;Department of Automation, Tsinghua University, Beijing, 100084, P.R. China
Venue:
RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Year:
2009

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Abstract

In this paper we present a novel method for object class recognition. A vocabulary of object parts is automatically constructed from sample images of the object class by AdaBoost. Images are then represented using parts from this vocabulary. Based on this representation, the Sparse Network of Winnows (SNoW) learning architecture is employed to learn to recognize instances of the object class. Experimental results show that the method achieves high recognition accuracy on different data sets, and is highly robust to partial occlusion and background clutter.