Proceedings of the International Conference on Logistics, Engineering, Management and Computer Science

Sparse Representation for Robust 3D Shape Matching

Authors
Hong Tu, Guohua Geng
Corresponding Author
Hong Tu
Available Online May 2014.
DOI
https://doi.org/10.2991/lemcs-14.2014.226How to use a DOI?
Keywords
sparse representation; matching; 3D shape; robust; large database
Abstract
With the number of 3D shapes has risen sharply, a fast and robust matching technology suitable for large 3D shape databases is one of the key technologies to enhance the retrieval performance. We proposed a general novel matching algorithm for 3D shape retrieval: SRRSM, based on sparse representation of signals. Using feature database of 3D shape as over-complete dictionary, the matching problem can be transfer to the problem of sparse representation of signals. It is a second-cone programming (SOCP) problem and can be solved in polynomial time by interior point methods. The proposed approach combines signal reconstruction, sparse and discrimination power in the objective function for matching. It is more sparse and robust for effective matching than the Euclidean distance the most commonly used for matching. Meanwhile, the proposed method is very suitable for large 3D shape database. Theoretical analysis and comparative experiment verify the efficacy of the proposed algorithm.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
International Conference on Logistics Engineering, Management and Computer Science (LEMCS 2014)
Part of series
Advances in Intelligent Systems Research
Publication Date
May 2014
ISBN
978-94-6252-010-3
ISSN
1951-6851
DOI
https://doi.org/10.2991/lemcs-14.2014.226How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Hong Tu
AU  - Guohua Geng
PY  - 2014/05
DA  - 2014/05
TI  - Sparse Representation for Robust 3D Shape Matching
BT  - International Conference on Logistics Engineering, Management and Computer Science (LEMCS 2014)
PB  - Atlantis Press
SP  - 1007
EP  - 1011
SN  - 1951-6851
UR  - https://doi.org/10.2991/lemcs-14.2014.226
DO  - https://doi.org/10.2991/lemcs-14.2014.226
ID  - Tu2014/05
ER  -