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Learning Class Specific Graph Prototypes

  • Shengping Xia
  • Edwin R. Hancock
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5716)

Abstract

This paper describes how to construct a graph prototype model from a large corpus of multi-view images using local invariant features. We commence by representing each image with a graph, which is constructed from a group of selected SIFT features. We then propose a new pairwise clustering method based on a graph matching similarity measure. The positive example graphs of a specific class accompanied with a set of negative example graphs are clustered into one or more clusters, which minimize an entropy function. Each cluster is simplified into a tree structure composed of a series of irreducible graphs, and for each of which a node co-occurrence probability matrix is obtained. Finally, a recognition oriented class specific graph prototype (CSGP) is automatically generated from the given graph set. Experiments are performed on over 50K training images spanning ~500 objects and over 20K test images of 68 objects. This demonstrates the scalability and recognition performance of our model.

Keywords

Learn Class Sift Feature Model View View Cluster Large Image Database 
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 2009

Authors and Affiliations

  • Shengping Xia
    • 1
  • Edwin R. Hancock
    • 2
  1. 1.ATR Lab, School of Electronic Science and EngineeringNational University of Defense TechnologyChangshaP.R. China
  2. 2.Department of Computer ScienceUniversity of YorkYorkUK

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