Sparse Representation for Robust 3D Shape Matching
Hong Tu, Guohua Geng
Available Online May 2014.
- https://doi.org/10.2991/lemcs-14.2014.226How to use a DOI?
- sparse representation; matching; 3D shape; robust; large database
- 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.
- 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 -