Context-aware discovery of visual co-occurrence patterns

Hongxing Wang, Junsong Yuan, Ying Wu

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


Once an image is decomposed into a number of visual primitives, e.g., local interest points or regions, it is of great interests to discover meaningful visual patterns from them. Conventional clustering of visual primitives, however, usually ignores the spatial and feature structure among them, thus cannot discover high-level visual patterns of complex structure. To overcome this problem, we propose to consider spatial and feature contexts among visual primitives for pattern discovery. By discovering spatial co-occurrence patterns among visual primitives and feature co-occurrence patterns among different types of features, our method can better address the ambiguities of clustering visual primitives. We formulate the pattern discovery problem as a regularized k-means clustering where spatial and feature contexts are served as constraints to improve the pattern discovery results. A novel self-learning procedure is proposed to utilize the discovered spatial or feature patterns to gradually refine the clustering result. Our self-learning procedure is guaranteed to converge and experiments on real images validate the effectiveness of our method.

Original languageEnglish (US)
Article number6748072
Pages (from-to)1805-1819
Number of pages15
JournalIEEE Transactions on Image Processing
Issue number4
StatePublished - Apr 2014


  • Clustering
  • feature context
  • spatial context
  • visual pattern discovery

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design


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