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

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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.

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