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

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 heavytailed 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.

• Type: Journal
• Year: 2010
• Where: TCSV
• Authors: Mohand Saïd Allili, Djemel Ziou, Nizar Bouguila, Sabri Boutemedjet
• Keywords: Clustering Model, Computational Biology, Feature Selection, Gaussian Mixture Modeling, TCSV 2010