Saliency Detection: A Boolean mAP Approach
Abstract
A novel Boolean Map based Saliency (BMS) model is proposed. An image is characterized by a set of binary images, which are generated by randomly thresholding the image's color channels. Based on a Gestalt principle of figure-ground segregation, BMS computes saliency maps by analyzing the topological structure of Boolean maps. BMS is simple to implement and efficient to run. Despite its simplicity, BMS consistently achieves state-of-the-art performance compared with ten leading methods on five eye tracking datasets. Furthermore, BMS is also shown to be advantageous in salient object detection.
Cite
Text
Zhang and Sclaroff. "Saliency Detection: A Boolean mAP Approach." International Conference on Computer Vision, 2013. doi:10.1109/ICCV.2013.26Markdown
[Zhang and Sclaroff. "Saliency Detection: A Boolean mAP Approach." International Conference on Computer Vision, 2013.](https://mlanthology.org/iccv/2013/zhang2013iccv-saliency/) doi:10.1109/ICCV.2013.26BibTeX
@inproceedings{zhang2013iccv-saliency,
title = {{Saliency Detection: A Boolean mAP Approach}},
author = {Zhang, Jianming and Sclaroff, Stan},
booktitle = {International Conference on Computer Vision},
year = {2013},
doi = {10.1109/ICCV.2013.26},
url = {https://mlanthology.org/iccv/2013/zhang2013iccv-saliency/}
}