By Topic

A Novel Feature Combination Methods for Saliency-Based Visual Attention

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
$31 $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

4 Author(s)
Bing Han ; VIPS Lab., Xidian Univ., Xi''an, China ; Tcheang, L. ; Walsh, V. ; Xinbo Gao

In the field of visual attention, bottom-up or saliency-based visual attention allows primates to detect non-specific conspicuous objects or targets in cluttered scenes. Simple multi-scale ¿feature maps¿ detect local spatial discontinuities in intensity, color, orientation, and are combined into a ¿saliency¿ map. In this paper, we propose a saliency map based on feature weighted, in which the rough sets is used to assign the weighting for every feature. This method measures the contribution of each conspicuity map obtained from the feature maps to saliency map. And it also carries out a dynamic weighting of individual conspicuity maps. We obtain results, which enrich the theory of saliency detection. We use the real data of natural scenes to demonstrate the effectiveness of the algorithm.

Published in:

Natural Computation, 2009. ICNC '09. Fifth International Conference on  (Volume:5 )

Date of Conference:

14-16 Aug. 2009