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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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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Abstract	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.
AbstractList	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.
Author	Jianming Zhang Sclaroff, Stan
Author_xml	– sequence: 1 surname: Jianming Zhang fullname: Jianming Zhang email: jmzhang@bu.edu organization: Dept. of Comput. Sci., Boston Univ., Boston, MA, USA – sequence: 2 givenname: Stan surname: Sclaroff fullname: Sclaroff, Stan email: sclaroff@cs.bu.edu organization: Dept. of Comput. Sci., Boston Univ., Boston, MA, USA
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/26336114$$D View this record in MEDLINE/PubMed
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Snippet	We demonstrate the usefulness of surroundedness for eye fixation prediction by proposing a Boolean Map based Saliency model (BMS). In our formulation, an image...
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SubjectTerms	Boolean map Computational modeling eye fixation prediction Image color analysis Machine intelligence minimum barrier distance Pattern recognition Predictive models Saliency detection Transforms Visualization
Title	Exploiting Surroundedness for Saliency Detection: A Boolean Map Approach
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