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Signal, Image and Video Processing

, Volume 12, Issue 5, pp 915–923 | Cite as

Heuristic optimization-based wave kernel descriptor for deformable 3D shape matching and retrieval

  • Seif Eddine Naffouti
  • Yohan Fougerolle
  • Ichraf Aouissaoui
  • Anis Sakly
  • Fabrice Mériaudeau
Original Paper

Abstract

This paper presents an optimized wave kernel signature (OWKS) using a modified particle swarm optimization (MPSO) algorithm. The variance parameter and its setting mode play a central role in this kernel. In order to circumvent a purely arbitrary choice of the internal parameters of the WKS algorithm, we present a four-step feature descriptor framework in an effort to further improve the classical wave kernel signature (WKS) by acting on its variance parameter. The advantage of the enhanced method comes from the tuning of the variance parameter using MPSO and the selection of the first vector from the constructed OWKS at its first energy scale, thus giving rise to substantially better matching and retrieval accuracy for deformable 3D shape. The special choice of this vector is to extremely reinforce the stability for efficient salient features extraction method from the 3D meshes. Experimental results demonstrate the effectiveness of our proposed shape classification and retrieval approach in comparison with state-of-the-art methods. For instance, in terms of the nearest neighbor (NN) metric, the OWKS achieves a 96.9% score, with performance improvements of 83.5 and 90.4% over the baseline methods WKS and heat kernel signature, respectively.

Keywords

Shapes and features classification Shape matching Shape retrieval Optimized wave kernel signature Heuristic optimization 

Notes

Acknowledgements

This work has been supported by the sponsorship of the European program in Vision Image and Robotics (VIBOT) (Erasmus Mundus) MSc under Grant No. 2012–2339/001-001-EM II-EMMC. The authors would like to thank A. Enis Cetin, Ph.D. Editor in Chief, Associate Editor and two anonymous reviewers who gave insightful comments and helpful suggestions for our manuscript.

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.LE2I FRE2005, CNRS, Arts et MétiersUniv. Bourgogne Franche-ComtéDijonFrance
  2. 2.Reseach Unit: Industrial Systems Study and Renewable Energy (ESIER), National Engineering School of Monastir (ENIM)University of MonastirMonastirTunisia
  3. 3.Centre for Intelligent Signal and Imaging Research (CISIR), Electrical and Electronic Engineering DepartmentUniversity Technology PetronasSeri IskandarMalaysia

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