By Topic

Region Diversity Maximization for Salient Object Detection

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Ran Shi ; School of Communication and Information Engineering, Shanghai University, Shanghai, China ; Zhi Liu ; Huan Du ; Xiang Zhang
more authors

Salient object detection is an important technique for many content-based applications, but it becomes a challenging work when handling the cluttered saliency maps, which cannot completely highlight salient object regions and cannot suppress background regions. In this letter, we propose a novel approach to detect salient object from saliency map without manually setting any parameters. Region diversity maximization is used as the objective function to direct the object detection, and the optimal window for locating the salient object is obtained using an efficient iterative search scheme. Experimental results on different saliency maps demonstrate the overall better detection performance and computational efficiency of our approach.

Published in:

IEEE Signal Processing Letters  (Volume:19 ,  Issue: 4 )