Journal article
Covariance-Based descriptors for efficient 3D shape matching, retrieval, and classification
IEEE Transactions on Multimedia, Vol.17(9), pp.1591-1603
2015
Abstract
State-of-the-art 3D shape classification and retrieval algorithms, hereinafter referred to as shape analysis, are often based on comparing signatures or descriptors that capture the main geometric and topological properties of 3D objects. None of the existing descriptors, however, achieve best performance on all shape classes. In this article, we explore, for the first time, the usage of covariance matrices of descriptors, instead of the descriptors themselves, in 3D shape analysis. Unlike histogram -based techniques, covariance-based 3D shape analysis enables the fusion and encoding of different types of features and modalities into a compact representation. Covariance matrices, however, are elements of the non-linear manifold of symmetric positive definite (SPD) matrices and thus \BBL2 metrics are not suitable for their comparison and clustering. In this article, we study geodesic distances on the Riemannian manifold of SPD matrices and use them as metrics for 3D shape matching and recognition. We then: (1) introduce the concepts of bag of covariance (BoC) matrices and spatially-sensitive BoC as a generalization to the Riemannian manifold of SPD matrices of the traditional bag of features framework, and (2) generalize the standard kernel methods for supervised classification of 3D shapes to the space of covariance matrices. We evaluate the performance of the proposed BoC matrices framework and covariance -based kernel methods and demonstrate their superiority compared to their descriptor-based counterparts in various 3D shape matching, retrieval, and classification setups.
Details
- Title
- Covariance-Based descriptors for efficient 3D shape matching, retrieval, and classification
- Authors/Creators
- H. Tabia (Author/Creator) - École Nationale Supérieure de l'Électronique et de ses ApplicationsH. Laga (Author/Creator) - University of South Australia
- Publication Details
- IEEE Transactions on Multimedia, Vol.17(9), pp.1591-1603
- Publisher
- IEEE
- Identifiers
- 991005544878807891
- Murdoch Affiliation
- Murdoch University
- Language
- English
- Resource Type
- Journal article
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- Collaboration types
- Domestic collaboration
- International collaboration
- Citation topics
- 4 Electrical Engineering, Electronics & Computer Science
- 4.17 Computer Vision & Graphics
- 4.17.245 3D Geometry Processing
- Web Of Science research areas
- Computer Science, Information Systems
- Computer Science, Software Engineering
- Telecommunications
- ESI research areas
- Computer Science