Title :
Saliency Detection: A Boolean Map Approach
Author :
Jianming Zhang ; Sclaroff, Stan
Author_Institution :
Dept. of Comput. Sci., Boston Univ., Boston, MA, USA
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.
Keywords :
Boolean functions; image colour analysis; object detection; BMS model; Boolean map approach; Boolean map based saliency model; Gestalt principle; binary images; eye tracking datasets; figure-ground segregation; image color channels; saliency detection; salient object detection; Feature extraction; Image color analysis; Kernel; Measurement; Object detection; Standards; Visualization; eye fixation; salient object detection; visual saliency;
Conference_Titel :
Computer Vision (ICCV), 2013 IEEE International Conference on
Conference_Location :
Sydney, NSW
DOI :
10.1109/ICCV.2013.26
