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Image Categorization Using Directed Graphs

  • Hua Wang
  • Heng Huang
  • Chris Ding
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6313)

Abstract

Most existing graph-based semi-supervised classification methods use pairwise similarities as edge weights of an undirected graph with images as the nodes of the graph. Recently several new graph construction methods produce, however, directed graph (asymmetric similarity between nodes). A simple symmetrization is often used to convert a directed graph to an undirected one. This, however, loses important structural information conveyed by asymmetric similarities. In this paper, we propose a novel symmetric co-linkage similarity which captures the essential relationship among the nodes in the directed graph. We apply this new co-linkage similarity in two important computer vision tasks for image categorization: object recognition and image annotation. Extensive empirical studies demonstrate the effectiveness of our method.

Keywords

Undirected Graph Recognition Accuracy Sparse Representation Image Categorization Image Annotation 
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 2010

Authors and Affiliations

  • Hua Wang
    • 1
  • Heng Huang
    • 1
  • Chris Ding
    • 1
  1. 1.Department of Computer Science and EngineeringUniversity of Texas at ArlingtonArlingtonUSA

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