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

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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.

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Authors and Affiliations

  1. College of Information System and Management, National University of Defense Technology, Changsha, 410073, China

    Lai Kang & Lingda Wu

  2. The Key Lab, the Academy of Equipment Command & Technology, Beijing, 101400, China

    Lingda Wu

  3. Department of Computing Science, University of Alberta, Edmonton, Alberta, T6G 2E8, Canada

    Lai Kang & Yee-Hong Yang

Authors
  1. Lai Kang
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  2. Lingda Wu
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  3. Yee-Hong Yang
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Corresponding author

Correspondence to Lai Kang.

Additional information

Lai Kang received the MS degree in systems engineering from the National University of Defense Technology, Changsha, China. He is currently pursuing the PhD degree from the same university. From September 2010 to August 2012, he is a visiting PhD student with the Computer Graphics Laboratory, Department of Computing Science, University of Alberta, Edmonton, Canada. His current research interests include computer vision with an emphasis on 3D reconstruction from images and global optimization.

Lingda Wu received the PhD Degree in management science and engineering from the National University of Defense Technology, Changsha, China. She is currently a professor at the Key Laboratory, the Academy of Equipment Command and Technology, Beijing, China. Her research focuses on multimedia information systems and virtual reality technology.

Yee-Hong Yang received the PhD degree from the University of Pittsburgh, Pittsburgh, PA. His research interests cover a wide spectrum of topics in computer vision and computer graphics. Topics in computer vision include animation, environment matting, hardware accelerated graphics, motion editing, physics-based modelling, texture analysis and synthesis, and static and dynamic image-based rendering. Topics in computer vision include edge detection, face detection and recognition, light source estimation, motion estimation, segmentation, 2D and 3D shape analysis, and real-time multiview stereo. Dr. Yang is a senior member of the IEEE and has published over 100 technical papers in international journals and conference Proceedings. He has served as reviewer to numerous international journals, as committee members to many conferences and review panels.

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Kang, L., Wu, L. & Yang, YH. A novel unsupervised approach for multilevel image clustering from unordered image collection. Front. Comput. Sci. 7, 69–82 (2013). https://doi.org/10.1007/s11704-013-1266-8

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  • DOI: https://doi.org/10.1007/s11704-013-1266-8

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