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

Naffouti, S.E. and Fougerolle, Y. and Aouissaoui, I. and Sakly, A. and Mériaudeau, F. (2018) Heuristic optimization-based wave kernel descriptor for deformable 3D shape matching and retrieval. Signal, Image and Video Processing, 12 (5). pp. 915-923.

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

Item Type:Article
Impact Factor:cited By 0
Uncontrolled Keywords:Deformation; Particle swarm optimization (PSO), Heat kernel signatures; Heuristic optimization; Kernel signatures; Modified particle swarm optimizations (MPSO); Performance improvements; Shape matching; Shape retrieval; State-of-the-art methods, Cryptography
ID Code:21479
Deposited By: Ahmad Suhairi
Deposited On:25 Sep 2018 06:31
Last Modified:25 Sep 2018 06:31

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