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Multimedia Tools and Applications

, Volume 78, Issue 21, pp 30943–30958 | Cite as

Multi-feature distance metric learning for non-rigid 3D shape retrieval

  • Huibing Wang
  • Haohao Li
  • Jinjia Peng
  • Xianping FuEmail author
Article

Abstract

In the past decades, feature-learning-based 3D shape retrieval approaches have been received widespread attention in the computer graphic community. These approaches usually explored the hand-crafted distance metric or conventional distance metric learning methods to compute the similarity of the single feature. The single feature always contains onefold geometric information, which cannot characterize the 3D shapes well. Therefore, the multiple features should be used for the retrieval task to overcome the limitation of single feature and further improve the performance. However, most conventional distance metric learning methods fail to integrate the complementary information from multiple features to construct the distance metric. To address these issue, a novel multi-feature distance metric learning method for non-rigid 3D shape retrieval is presented in this study, which can make full use of the complimentary geometric information from multiple shape features by utilizing the KL-divergences. Minimizing KL-divergence between different metric of features and a common metric is a consistency constraints, which can lead the consistency shared latent feature space of the multiple features. We apply the proposed method to 3D model retrieval, and test our method on well known benchmark database. The results show that our method substantially outperforms the state-of-the-art non-rigid 3D shape retrieval methods.

Keywords

Multi-view learning Distance metric learning Non-rigid 3D shape retrieval 

Notes

Acknowledgments

This study was funded by the National Natural Science Foundation of China Grant 61370142 and Grant 61272368, by the Fundamental Research Funds for the Central Universities Grant 3132016352, by the Fundamental Research of Ministry of Transport of P.R. China Grant 2015329225300. Huibing Wang, Haohao Li and Xianping Fu declare that they have no conflict of interest. Huibing Wang and Haohao Li contribute equally to this article. This article does not contain any studies with human participants or animals performed by any of the authors.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Huibing Wang
    • 1
  • Haohao Li
    • 2
  • Jinjia Peng
    • 1
  • Xianping Fu
    • 1
    Email author
  1. 1.College of Information and Science TechnologyDalian Maritime UniversityDalianChina
  2. 2.School of Mathematical SciencesDalian University of TechnologyDalianChina

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