Image Clustering via Sparse Representation

  • Jun Jiao
  • Xuan Mo
  • Chen Shen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5916)


In recent years, clustering techniques have become a useful tool in exploring data structures and have been employed in a broad range of applications. In this paper we derive a novel image clustering approach based on a sparse representation model, which assumes that each instance can be reconstructed by the sparse linear combination of other instances. Our method characterizes the graph adjacency structure and graph weights by sparse linear coefficients computed by solving ℓ1-minimization. Spectral clustering algorithm using these coefficients as graph weight matrix is then used to discover the cluster structure. Experiments confirmed the effectiveness of our approach.


Image Clustering Spectral Clustering Sparse Representation 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Jun Jiao
    • 1
  • Xuan Mo
    • 2
  • Chen Shen
    • 3
  1. 1.Computer Science & Technology DepartmentNanjing UniversityNanjingChina
  2. 2.Department of AutomationTsinghua UniversityBeijingChina
  3. 3.School of Software and ElectronicsPeking UniversityBeijingChina

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