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Local commute-time guided MDS for 3D non-rigid object retrieval

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Abstract

In this paper, we propose a 3D non-rigid shape retrieval method based on canonical shape analysis. Our main idea is to transform the problem of non-rigid shape retrieval into a rigid shape retrieval problem via the well-known multidimensional scaling (MDS) approach and random walk on graphs. We first segment the non-rigid shape into local partitions based on its salient features. Then, we calculate a local MDS problem for each partition, where the local commute time distance is used as weighting function in order to preserve local shape details. Finally, we aggregate the set of local MDS problems as a global constrained problem. The constraint is formulated using the biharmonic function between local salient features. In contrast to MDS method, the proposed local MDS is computationally efficient, parameters free and gives isometry-invariant forms with minimum features distortion. Due to these advantageous properties, the proposed method achieved good retrieval accuracy on non-rigid shape benchmark datasets.

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Authors and Affiliations

  1. MARS Research Lab Higher Institute of Computer Science and Telecom (ISITCom), University of Sousse, Sousse, Tunisia

    Hela Haj Mohamed, Samir Belaid & Lotfi Ben Romdhane

  2. University of Tunis El Manar, Tunis, Tunisia

    Wady Naanaa

Authors
  1. Hela Haj Mohamed
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  2. Samir Belaid
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  3. Wady Naanaa
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  4. Lotfi Ben Romdhane
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Correspondence to Hela Haj Mohamed.

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Haj Mohamed, H., Belaid, S., Naanaa, W. et al. Local commute-time guided MDS for 3D non-rigid object retrieval. Appl Intell 48, 2873–2883 (2018). https://doi.org/10.1007/s10489-017-1114-x

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