By Topic

Supervised image segmentation via ground truth decomposition

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

3 Author(s)
Levner, I. ; Dept. of Comput. Sci., Univ. of Alberta, Edmonton, AB ; Greiner, R. ; Hong Zhang

This paper proposes a data driven image segmentation algorithm, based on decomposing the target output (ground truth). Classical pixel labeling methods utilize machine learning algorithms that induce a mapping from pixel features to individual pixel labels. In contrast we propose to first extract features from both images and labels. Subsequently we induce a mapping from pixel features to label features and synthesize the final output by combining the newly derived label components. We demonstrate the effectiveness of the proposed approach by applying log-Gabor filters to both input and ground truth images of mineral ore. Subsequently we train perceptrons and regression trees to produce individual output components that are combined in frequency space to create the final segmentation. Experimental results show significant improvements over contextual pixel labeling and over ensemble methods.

Published in:

Image Processing, 2008. ICIP 2008. 15th IEEE International Conference on

Date of Conference:

12-15 Oct. 2008