Share Email Print

Proceedings Paper • Open Access

Local visual similarity descriptor for describing local region

Paper Abstract

Many works have devoted to exploring local region information including both the information of the local features in local region and their spatial relationships, but none of these can provide a compact representation of the information. To achieve this, we propose a new approach named Local Visual Similarity (LVS). LVS first calculates the similarities among the local features in a local region and then forms these similarities as a single vector named LVS descriptor. In our experiments, we show that LVS descriptor can preserve local region information with low dimensionality. Besides, experimental results on two public datasets also demonstrate the effectiveness of LVS descriptor.

Paper Details

Date Published: 17 March 2017
PDF: 6 pages
Proc. SPIE 10341, Ninth International Conference on Machine Vision (ICMV 2016), 103410S (17 March 2017); doi: 10.1117/12.2268689
Show Author Affiliations
Xianglin Huang, Communication Univ. of China (China)
Ye Xu, Communication Univ. of China (China)
Lifang Yang, Communication Univ. of China (China)

Published in SPIE Proceedings Vol. 10341:
Ninth International Conference on Machine Vision (ICMV 2016)
Antanas Verikas; Petia Radeva; Dmitry P. Nikolaev; Wei Zhang; Jianhong Zhou, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?