Object categorization based on a supervised mean shift algorithm

Authors:
Ruo Du;Qiang Wu;Xiangjian He;Jie Yang
Affiliations:
University of Technology, Sydney, Australia;University of Technology, Sydney, Australia;University of Technology, Sydney, Australia;Shanghai Jiaotong University, China
Venue:
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
Year:
2012

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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.