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

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 algorith...

Full description

Main Authors: Naffouti, S.E., Fougerolle, Y., Aouissaoui, I., Sakly, A., Mériaudeau, F.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.21479 /
Published: Springer London 2018
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85040784795&doi=10.1007%2fs11760-018-1235-7&partnerID=40&md5=9670012804870df62ca9d4e190cd4477
http://eprints.utp.edu.my/21479/
Tags: Add Tag
No Tags, Be the first to tag this record!
id utp-eprints.21479
recordtype eprints
spelling utp-eprints.214792018-09-25T06:31:48Z Heuristic optimization-based wave kernel descriptor for deformable 3D shape matching and retrieval Naffouti, S.E. Fougerolle, Y. Aouissaoui, I. Sakly, A. Mériaudeau, F. 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. Springer London 2018 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85040784795&doi=10.1007%2fs11760-018-1235-7&partnerID=40&md5=9670012804870df62ca9d4e190cd4477 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. http://eprints.utp.edu.my/21479/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description 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.
format Article
author Naffouti, S.E.
Fougerolle, Y.
Aouissaoui, I.
Sakly, A.
Mériaudeau, F.
spellingShingle Naffouti, S.E.
Fougerolle, Y.
Aouissaoui, I.
Sakly, A.
Mériaudeau, F.
Heuristic optimization-based wave kernel descriptor for deformable 3D shape matching and retrieval
author_sort Naffouti, S.E.
title Heuristic optimization-based wave kernel descriptor for deformable 3D shape matching and retrieval
title_short Heuristic optimization-based wave kernel descriptor for deformable 3D shape matching and retrieval
title_full Heuristic optimization-based wave kernel descriptor for deformable 3D shape matching and retrieval
title_fullStr Heuristic optimization-based wave kernel descriptor for deformable 3D shape matching and retrieval
title_full_unstemmed Heuristic optimization-based wave kernel descriptor for deformable 3D shape matching and retrieval
title_sort heuristic optimization-based wave kernel descriptor for deformable 3d shape matching and retrieval
publisher Springer London
publishDate 2018
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85040784795&doi=10.1007%2fs11760-018-1235-7&partnerID=40&md5=9670012804870df62ca9d4e190cd4477
http://eprints.utp.edu.my/21479/
_version_ 1741196466494373888
score 11.62408

Similar Items