Object Categorization Based on a Supervised Mean Shift Algorithm

Ruo Du¹, Qiang Wu¹, Xiangjian He¹, and Jie Yang²

¹University of Technology, Sydney, Australia

²Shanghai Jiaotong University, China

Abstract. In this work, we present a C++ implementation of object categorization with the bag-of-word (BoW) framework. Unlike typical BoW models which consider the whole area of an image as the region of interest (ROI) for visual codebook generation, our implementation only considers the regions of target objects as ROIs and the unrelated backgrounds will be excluded for generating codebook. This is achieved by a supervised mean shift algorithm. Our work is on the benchmark SIVAL dataset and utilizes a Maximum Margin Supervised Topic Model for classification. The final performance of our work is quite encouraging.

LNCS 7585, p. 611 ff.

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