Graph Degree Linkage: Agglomerative Clustering on a Directed Graph

  • Wei Zhang
  • Xiaogang Wang
  • Deli Zhao
  • Xiaoou Tang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7572)


This paper proposes a simple but effective graph-based agglomerative algorithm, for clustering high-dimensional data. We explore the different roles of two fundamental concepts in graph theory, indegree and outdegree, in the context of clustering. The average indegree reflects the density near a sample, and the average outdegree characterizes the local geometry around a sample. Based on such insights, we define the affinity measure of clusters via the product of average indegree and average outdegree. The product-based affinity makes our algorithm robust to noise. The algorithm has three main advantages: good performance, easy implementation, and high computational efficiency. We test the algorithm on two fundamental computer vision problems: image clustering and object matching. Extensive experiments demonstrate that it outperforms the state-of-the-arts in both applications.


Pairwise Distance Spectral Cluster Normalize Mutual Information Agglomerative Cluster Image Cluster 
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

  • Wei Zhang
    • 1
  • Xiaogang Wang
    • 2
    • 3
  • Deli Zhao
    • 1
  • Xiaoou Tang
    • 1
    • 3
  1. 1.Department of Information EngineeringThe Chinese University of Hong KongHong Kong
  2. 2.Department of Electronic EngineeringThe Chinese University of Hong KongHong Kong
  3. 3.Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesChina

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