Saliency Weighted Features for Person Re-Identification
Abstract
In this work we propose a novel person re-identification approach. The solution, inspired by human gazing capabilities, wants to identify the salient regions of a given person. Such regions are used as a weighting tool in the image feature extraction process. Then, such novel representation is combined with a set of other visual features in a pairwise-based multiple metric learning framework. Finally, the learned metrics are fused to get the distance between image pairs and to re-identify a person. The proposed method is evaluated on three different benchmark datasets and compared with best state-of-the-art approaches to show its overall superior performance.
Cite
Text
Martinel et al. "Saliency Weighted Features for Person Re-Identification." European Conference on Computer Vision, 2014. doi:10.1007/978-3-319-16199-0_14Markdown
[Martinel et al. "Saliency Weighted Features for Person Re-Identification." European Conference on Computer Vision, 2014.](https://mlanthology.org/eccv/2014/martinel2014eccv-saliency/) doi:10.1007/978-3-319-16199-0_14BibTeX
@inproceedings{martinel2014eccv-saliency,
title = {{Saliency Weighted Features for Person Re-Identification}},
author = {Martinel, Niki and Micheloni, Christian and Foresti, Gian Luca},
booktitle = {European Conference on Computer Vision},
year = {2014},
pages = {191-208},
doi = {10.1007/978-3-319-16199-0_14},
url = {https://mlanthology.org/eccv/2014/martinel2014eccv-saliency/}
}