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Full and partial shape similarity through sparse descriptor reconstruction

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Abstract

We introduce a novel approach to measuring similarity between two shapes based on sparse reconstruction of shape descriptors. The main feature of our approach is its applicability in situations where either of the two shapes may have moderate to significant portions of its data missing. Let the two shapes be A and B. Without loss of generality, we characterize A by learning a sparse dictionary from its local descriptors. The similarity between A and B is defined by the error incurred when reconstructing B’s descriptor set using the basis signals from A’s dictionary. Benefits of using sparse dictionary learning and reconstruction are twofold. First, sparse dictionary learning reduces data redundancy and facilitates similarity computations. More importantly, the reconstruction error is expected to be small as long as B is similar to A, regardless of whether the similarity is full or partial. Our proposed approach achieves significant improvements over previous works when retrieving non-rigid shapes with missing data, and it is also comparable to state-of-the-art methods on the retrieval of complete non-rigid shapes.

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Acknowledgments

We would like to thank the anonymous reviewers for their comments and constructive suggestions. Thanks also go to Warunika Ranaweera, Wallace Lira and Rui Ma for their careful proofreading.

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Authors and Affiliations

  1. Institute of Information Science, Beijing Jiaotong University, Beijing, China

    Lili Wan

  2. Hengyang Normal University, Hengyang, China

    Changqing Zou

  3. Simon Fraser University, Burnaby, Canada

    Changqing Zou & Hao Zhang

Authors
  1. Lili Wan
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  2. Changqing Zou
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  3. Hao Zhang
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Corresponding authors

Correspondence to Lili Wan or Changqing Zou.

Additional information

The work is supported in part by grants from China Scholarship Council, National Natural Science Foundation of China (61572064 and 61502153), the Fundamental Research Funds for the Central Universities of China (2014JBM027), Natural Science Foundation of Hunan Province of China (2016JJ3031), National 973 Program (2011CB302203) and NSERC (611370).

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Wan, L., Zou, C. & Zhang, H. Full and partial shape similarity through sparse descriptor reconstruction. Vis Comput 33, 1497–1509 (2017). https://doi.org/10.1007/s00371-016-1293-1

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  • DOI: https://doi.org/10.1007/s00371-016-1293-1

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