Saliency Detection: A Boolean Map Approach

Jianming Zhang, Stan Sclaroff; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2013, pp. 153-160


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.

Related Material

author = {Zhang, Jianming and Sclaroff, Stan},
title = {Saliency Detection: A Boolean Map Approach},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},
month = {December},
year = {2013}