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

Authors:
M. S. Allili;D. Ziou;N. Bouguila;S. Boutemedjet
Affiliations:
Dept. of Comput. Sci. & Eng., Univ. du Quebec en Outaouais, Gatineau, QC, Canada;-;-;-
Venue:
IEEE Transactions on Circuits and Systems for Video Technology
Year:
2010

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Abstract

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.