In-plane rotation and scale invariant clustering using dictionaries

Yi Chen Chen, Challa S. Sastry, Vishal M. Patel, P. Jonathon Phillips, Rama Chellappa

Research output: Contribution to journalArticlepeer-review


In this paper, we present an approach that simultaneously clusters images and learns dictionaries from the clusters. The method learns dictionaries and clusters images in the radon transform domain. The main feature of the proposed approach is that it provides both in-plane rotation and scale invariant clustering, which is useful in numerous applications, including content-based image retrieval (CBIR). We demonstrate the effectiveness of our rotation and scale invariant clustering method on a series of CBIR experiments. Experiments are performed on the Smithsonian isolated leaf, Kimia shape, and Brodatz texture datasets. Our method provides both good retrieval performance and greater robustness compared to standard Gabor-based and three state-of-the-art shape-based methods that have similar objectives.

Original languageEnglish (US)
Article number6459017
Pages (from-to)2166-2180
Number of pages15
JournalIEEE Transactions on Image Processing
Issue number6
StatePublished - 2013
Externally publishedYes


  • Clustering
  • content-based image retrieval (CBIR)
  • dictionary learning
  • radon transform
  • rotation invariance
  • scale invariance

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design


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