Saliency Detection: A Boolean Map Approach

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 topolo...

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Bibliographic Details
Published in2013 IEEE International Conference on Computer Vision pp. 153 - 160
Main Authors Jianming Zhang, Sclaroff, Stan
Format Conference Proceeding Journal Article
LanguageEnglish
Published IEEE 01.12.2013
Subjects
Boolean algebra
Channels
Color
Computer vision
Conferences
eye fixation
Feature extraction
Image color analysis
Kernel
Measurement
Object detection
salient object detection
Segregations
State of the art
Tracking
visual saliency
Visualization
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Summary: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.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Conference-1
ObjectType-Feature-3
content type line 23
SourceType-Conference Papers & Proceedings-2
ISSN:1550-5499
DOI:10.1109/ICCV.2013.26