Exploiting Surroundedness for Saliency Detection: A Boolean Map Approach

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...

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Bibliographic Details
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, 2160-9292, 1939-3539
Online Access:Get full text
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Summary: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.
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ISSN:0162-8828
2160-9292
1939-3539
DOI:10.1109/TPAMI.2015.2473844