Title: A 3D shape matching and retrieval approach based on fusion of curvature and geometric diffusion features
Authors: Bilal Mokhtari; Kamal Eddine Melkemi; Dominique Michelucci; Sebti Foufou
Addresses: Department of Computer Science, University of Biskra, BP145 RP, 07000 Biskra, Algeria; LE2I UMR6306, CNRS, Univ. Bourgogne Franche - Comté, 21000 Dijon, France ' Department of Computer Science, University of Biskra, BP145 RP, 07000 Biskra, Algeria ' LE2I UMR6306, CNRS, Univ. Bourgogne Franche - Comté, 21000 Dijon, France ' CSE Department, College of Engineering, Qatar University, P.O. Box 2713, Doha, Qatar
Abstract: The majority of shape matching and retrieval methods use only one single shape descriptor. Unfortunately, no shape descriptor is sufficient to provide suitable results for all kinds of shapes. The most common way to improve the performance of shape descriptors is to fuse them. In this paper, we propose a new 3D matching and retrieval approach based on a fully unsupervised fusion of curvature and geometric diffusion descriptors. In fact, to improve retrieval precision, we use two descriptors based on local and global features extracted from a shape, and automatically combine these features using a fusion method called Product rule. The Product rule combines values assigned to vertices by the two descriptors. This fusion rule gives better results compared to other well-known fusion schemes such as Max, Min and Linear rules. The proposed approach improves considerably the retrieval precision even with pose changes. This is shown through the retrieval results obtained on several popular 3D shape benchmarks.
Keywords: 3D shape matching; 3D shape retrieval; curvature-based features; diffusion geometry; combination schemes; feature fusion; 3D shape descriptors; triangular meshes; similarity measures; features extraction.
International Journal of Computer Applications in Technology, 2017 Vol.55 No.2, pp.79 - 91
Received: 06 Feb 2016
Accepted: 24 May 2016
Published online: 14 Mar 2017 *