A Local Sparse Model for Matching Problem

Bo Jiang, Jin Tang, Chris H. Q. Ding, Bin Luo

AAAI 2015 pp. 3790-3796

doi:10.1609/AAAI.V29I1.9785 /aaai/2015/jiang2015aaai-local/

Abstract

Feature matching problem that incorporates pairwise constraints is usually formulated as a quadratic assignment problem (QAP). Since it is NP-hard, relaxation models are required. In this paper, we first formulate the QAP from the match selection point of view; and then propose a local sparse model for matching problem. Our local sparse matching (LSM) method has the following advantages: (1) It is parameter-free; (2) It generates a local sparse solution which is closer to a discrete matrix than most other continuous relaxation methods for the matching problem. (3) The one-to-one matching constraints are better maintained in LSM solution. Promising experimental results show the effectiveness of the Proposed LSM method.

PDF AAAI Semantic Scholar

Cite

Text

Jiang et al. "A Local Sparse Model for Matching Problem." AAAI Conference on Artificial Intelligence, 2015. doi:10.1609/AAAI.V29I1.9785

Markdown

[Jiang et al. "A Local Sparse Model for Matching Problem." AAAI Conference on Artificial Intelligence, 2015.](https://mlanthology.org/aaai/2015/jiang2015aaai-local/) doi:10.1609/AAAI.V29I1.9785

BibTeX

@inproceedings{jiang2015aaai-local,
  title     = {{A Local Sparse Model for Matching Problem}},
  author    = {Jiang, Bo and Tang, Jin and Ding, Chris H. Q. and Luo, Bin},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2015},
  pages     = {3790-3796},
  doi       = {10.1609/AAAI.V29I1.9785},
  url       = {https://mlanthology.org/aaai/2015/jiang2015aaai-local/}
}