Notification:
We are currently experiencing intermittent issues impacting performance. We apologize for the inconvenience.
By Topic

Image and Video Segmentation by Combining Unsupervised Generalized Gaussian Mixture Modeling and Feature Selection

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)
Allili, M.S. ; Dept. of Comput. Sci. & Eng., Univ. du Quebec en Outaouais, Gatineau, QC, Canada ; Ziou, D. ; Bouguila, N. ; Boutemedjet, S.

In this letter, we propose a clustering model that efficiently mitigates image and video under/over-segmentation by combining generalized Gaussian mixture modeling and feature selection. The model has flexibility to accurately represent heavy-tailed image/video histograms, while automatically discarding uninformative features, leading to better discrimination and localization of regions in high-dimensional spaces. Experimental results on a database of real-world images and videos showed us the effectiveness of the proposed approach.

Published in:

Circuits and Systems for Video Technology, IEEE Transactions on  (Volume:20 ,  Issue: 10 )