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Efficiently finding unusual shapes in large image databases

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Abstract

Among the visual features of multimedia content, shape is of particular interest because humans can often recognize objects solely on the basis of shape. Over the past three decades, there has been a great deal of research on shape analysis, focusing mostly on shape indexing, clustering, and classification. In this work, we introduce the new problem of finding shape discords, the most unusual shapes in a collection. We motivate the problem by considering the utility of shape discords in diverse domains including zoology, microscopy, anthropology, and medicine. While the brute force search algorithm has quadratic time complexity, we avoid this untenable lethargy by using locality-sensitive hashing to estimate similarity between shapes which enables us to reorder the search more efficiently and thus extract the maximum benefit from an admissible pruning strategy we introduce. An extensive experimental evaluation demonstrates that our approach is empirically linear in time.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, University of California, Riverside, CA, 92521, USA

    Li Wei, Eamonn Keogh & Xiaopeng Xi

  2. Department of Entomology, University of California, Riverside, CA, 92521, USA

    Melissa Yoder

Authors
  1. Li Wei
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  2. Eamonn Keogh
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  3. Xiaopeng Xi
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  4. Melissa Yoder
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Corresponding author

Correspondence to Li Wei.

Additional information

Responsible editor: M. J. Zaki.

A primary version of this work appears in ICDM 2006.

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Wei, L., Keogh, E., Xi, X. et al. Efficiently finding unusual shapes in large image databases. Data Min Knowl Disc 17, 343–376 (2008). https://doi.org/10.1007/s10618-008-0094-1

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  • DOI: https://doi.org/10.1007/s10618-008-0094-1

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