Universal joint image clustering and registration using multivariate information measures

Ravi Kiran Raman, Lav R. Varshney

Research output: Contribution to journalArticlepeer-review


We consider the problem of universal joint clustering and registration of images. Image clustering focuses on grouping similar images, while image registration refers to the task of aligning copies of an image that have been subject to rigid-body transformations, such as rotations and translations. We first study registering two images using maximum mutual information and prove its asymptotic optimality. We then show the shortcomings of pairwise registration in multi-image registration, and design an asymptotically optimal algorithm based on multi-information. Further, we define a novel multivariate information functional to perform joint clustering and registration of images, and prove consistency of the algorithm. Finally, we consider registration and clustering of numerous limited-resolution images, defining algorithms that are order-optimal in scaling of number of pixels in each image with the number of images.

Original languageEnglish (US)
Article number8409946
Pages (from-to)928-943
Number of pages16
JournalIEEE Journal on Selected Topics in Signal Processing
Issue number5
StatePublished - Oct 2018


  • Image registration
  • asymptotic optimality
  • clustering
  • universal information theory
  • unsupervised learning

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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