Image clustering based on sparse patch alignment framework

Jun Yu, Richang Hong, Meng Wang, Jia You

Research output: Journal article publicationJournal articleAcademic researchpeer-review

100 Citations (Scopus)


Image clustering methods are efficient tools for applications such as content-based image retrieval and image annotation. Recently, graph based manifold learning methods have shown promising performance in extracting features for image clustering. Typical manifold learning methods adopt appropriate neighborhood size to construct the neighborhood graph, which captures local geometry of data distribution. Because the density of data points' distribution may be different in different regions of the manifold, a fixed neighborhood size may be inappropriate in building the manifold. In this paper, we propose a novel algorithm, named sparse patch alignment framework, for the embedding of data lying in multiple manifolds. Specifically, we assume that for each data point there exists a small neighborhood in which only the points that come from the same manifold lie approximately in a low-dimensional affine subspace. Based on the patch alignment framework, we propose an optimization strategy for constructing local patches, which adopt sparse representation to select a few neighbors of each data point that span a low-dimensional affine subspace passing near that point. After that, the whole alignment strategy is utilized to build the manifold. Experiments are conducted on four real-world datasets, and the results demonstrate the effectiveness of the proposed method.
Original languageEnglish
Pages (from-to)3512-3519
Number of pages8
JournalPattern Recognition
Issue number11
Publication statusPublished - 1 Jan 2014


  • Image clustering
  • Manifold learning
  • Sparse representation

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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