Exploiting Surroundedness for Saliency Detection: A Boolean Map Approach

• Published in: IEEE transactions on pattern analysis and machine intelligence Vol. 38; no. 5; pp. 889 - 902
• Main Authors: Jianming Zhang, Sclaroff, Stan
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.05.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Boolean map; Computational modeling; eye fixation prediction; Image color analysis; Machine intelligence; minimum barrier distance; Pattern recognition; Predictive models; Saliency detection; Transforms; Visualization; Boolean map; minimum barrier distance; eye fixation prediction; Saliency detection
• ISSN: 0162-8828; 1939-3539; 2160-9292
• DOI: 10.1109/TPAMI.2015.2473844

We demonstrate the usefulness of surroundedness for eye fixation prediction by proposing a Boolean Map based Saliency model (BMS). In our formulation, an image is characterized by a set of binary images, which are generated by randomly thresholding the image's feature maps in a whitened feature space. Based on a Gestalt principle of figure-ground segregation, BMS computes a saliency map by discovering surrounded regions via topological analysis of Boolean maps. Furthermore, we draw a connection between BMS and the Minimum Barrier Distance to provide insight into why and how BMS can properly captures the surroundedness cue via Boolean maps. The strength of BMS is verified by its simplicity, efficiency and superior performance compared with 10 state-of-the-art methods on seven eye tracking benchmark datasets.