By Topic

Segmentation by Fusion of Features in Multiple Color Spaces and Texture Features Based on PRI

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)
Liangmei Hu ; Sch. of Comput. & Inf., Hefei Univ. of Technol., Hefei, China ; Lili Zhang ; Zhumeng Wang ; Xudong Zhang

For natural image segmentation, due to features from a single image are hard to describe the complex scene information, this paper presents a new method based on the fusion model evaluation index PRI to fuse color histogram features in 3 color spaces, RGB, XYZ, LUV, and texture features. We experiment on images from Berkeley segmentation databases and compare the quantitative and qualitative experimental results with manual segmentation and some classic segmentation methods, such as Mean-shift, FCR, etc. Experimental results show that the results of this paper are more similar to the real segmentation results of manual segmentations. The method proposed by this paper has obvious advantages in solving the contradiction between segmentation accuracy and robustness, and the contradiction between over-segmentation and insufficient segmentation.

Published in:

Photonics and Optoelectronics (SOPO), 2011 Symposium on

Date of Conference:

16-18 May 2011