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• W2051205321 abstract "In this paper we investigate the integration of feature selection in segmentation through an unsupervised learning approach. We propose a clustering algorithm that efficiently mitigates image under/over-segmentation, by combining generalized Gaussian mixture modeling and feature selection. The algorithm is based on generalized Gaussian mixture modeling which is less prone to region number over-estimation in case of noisy and heavy-tailed image distributions. On the other hand, our feature selection mechanism allows to automatically discard uninformative features, which leads to better discrimination and localization of regions in high-dimensional spaces. Experimental results on a large database of real-world images showed us the effectiveness of the proposed approach." @default.
• W2051205321 created "2016-06-24" @default.
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• W2051205321 date "2010-01-01" @default.
• W2051205321 modified "2023-09-26" @default.
• W2051205321 title "Unsupervised Feature Selection and Learning for Image Segmentation" @default.
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• W2051205321 doi "https://doi.org/10.1109/crv.2010.44" @default.
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