By Topic

A Fast Framework for Objects Cursory Recognition in Cluster Scene Based on 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
$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

2 Author(s)
Minghao Yang ; Inst. of Autom., Chinese Acad. of Sci., Beijing ; Yangsheng Wang

This paper presents a real-time framework for objects cursory recognition in cluster scene based on visual attention. First, multi-scale image features are combined into a single saliency map. Then, k-means method is used to estimate the position of objects from cluster scene by saliency map. Finally, we construct global color feature vector for saliency regions and recognize the objects by their correlation coefficients with templates. Results shows that this framework is efficient for objects cursory recognition in random cluster scene.

Published in:

Computer Science and Software Engineering, 2008 International Conference on  (Volume:1 )

Date of Conference:

12-14 Dec. 2008