Local visual similarity descriptor for describing local region

Xianglin Huang; Ye Xu; Lifang Yang

doi:10.1117/12.2268689

17 March 2017 Local visual similarity descriptor for describing local region

Xianglin Huang, Ye Xu, Lifang Yang

Author Affiliations +

Proceedings Volume 10341, Ninth International Conference on Machine Vision (ICMV 2016); 103410S (2017) https://doi.org/10.1117/12.2268689
Event: Ninth International Conference on Machine Vision, 2016, Nice, France

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.

Citation Download Citation

Xianglin Huang, Ye Xu, and Lifang Yang "Local visual similarity descriptor for describing local region", Proc. SPIE 10341, Ninth International Conference on Machine Vision (ICMV 2016), 103410S (17 March 2017); https://doi.org/10.1117/12.2268689

ACCESS THE FULL ARTICLE

PROCEEDINGS
6 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY