A novel unsupervised approach for multilevel image clustering from unordered image collection

  • Authors:

  • Lai Kang;Lingda Wu;Yee-Hong Yang

  • Affiliations:

  • College of Information System and Management, National University of Defense Technology, Changsha, China 410073 and Department of Computing Science, University of Alberta, Edmonton, Canada T6G 2E8;College of Information System and Management, National University of Defense Technology, Changsha, China 410073 and The Key Lab, the Academy of Equipment Command & Technology, Beijing, China 10140 ...;Department of Computing Science, University of Alberta, Edmonton, Canada T6G 2E8

  • Venue:
  • Frontiers of Computer Science: Selected Publications from Chinese Universities
  • Year:
  • 2013

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Abstract

A novel unsupervised approach to automatically constructing multilevel image clusters from unordered images is proposed in this paper. The whole input image collection is represented as an imaging sample space (ISS) consisting of globally indexed image features extracted by a new efficient multi-view image feature matching method. By making an analogy between image capturing and observation of ISS, each image is represented as a binary sequence, in which each bit indicates the visibility of a corresponding feature. Based on information theory-inspired image popularity and dissimilarity measures, we show that the image content and distance can be quantitatively described, guided by which an input image collection is organized into multilevel clusters automatically. The effectiveness and the efficiency of the proposed approach are demonstrated using three real image collections and promising results were obtained from both qualitative and quantitative evaluation.