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Object Categorization Based on a Supervised Mean Shift Algorithm

  • Ruo Du
  • Qiang Wu
  • Xiangjian He
  • Jie Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7585)

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.

Keywords

Target Object Visual Word Latent Dirichlet Allocation Object Categorization Feature Code 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Ruo Du
    • 1
  • Qiang Wu
    • 1
  • Xiangjian He
    • 1
  • Jie Yang
    • 2
  1. 1.University of TechnologySydneyAustralia
  2. 2.Shanghai Jiaotong UniversityChina

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