NDLI: Statistical Textural Distinctiveness for Salient Region Detection in Natural Images

Content Provider	IEEE Xplore Digital Library
Author	Scharfenberger, C. Wong, A. Fergani, K. Zelek, J.S. Clausi, D.A.
Copyright Year	2013
Description	Author affiliation: Vision & Image Process. (VIP) Res. Group, Univ. of Waterloo, Waterloo, ON, Canada (Scharfenberger, C.; Wong, A.; Fergani, K.; Zelek, J.S.; Clausi, D.A.)
Abstract	A novel statistical textural distinctiveness approach for robustly detecting salient regions in natural images is proposed. Rotational-invariant neighborhood-based textural representations are extracted and used to learn a set of representative texture atoms for defining a sparse texture model for the image. Based on the learnt sparse texture model, a weighted graphical model is constructed to characterize the statistical textural distinctiveness between all representative texture atom pairs. Finally, the saliency of each pixel in the image is computed based on the probability of occurrence of the representative texture atoms, their respective statistical textural distinctiveness based on the constructed graphical model, and general visual attentive constraints. Experimental results using a public natural image dataset and a variety of performance evaluation metrics show that the proposed approach provides interesting and promising results when compared to existing saliency detection methods.
Starting Page	979
Ending Page	986
File Size	3051091
Page Count	8
File Format	PDF
ISBN	9780769549897
ISSN	10636919
DOI	10.1109/CVPR.2013.131
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-06-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computational modeling Visualization Graphical models Image color analysis Image segmentation Probability Computational complexity low level image processing Statistical textural distinctiveness saliency computation sparse texture model
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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