@article{allili_image_2010,

title = {Image and video segmentation by combining unsupervised generalized Gaussian mixture modeling and feature selection},

author = {M. S. Allili and D. Ziou and N. Bouguila and S. Boutemedjet},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-77957964550&doi=10.1109%2fTCSVT.2010.2077483&partnerID=40&md5=d888c7fe52eff37a5744bccd6a4d3d9e},

doi = {10.1109/TCSVT.2010.2077483},

issn = {10518215},

year  = {2010},

date = {2010-01-01},

journal = {IEEE Transactions on Circuits and Systems for Video Technology},

volume = {20},

number = {10},

pages = {1373–1377},

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. © 2010 IEEE.},

keywords = {Clustering model, Feature extraction, Feature selection, Gaussian distribution, Generalized Gaussian, Heavy-tailed, High dimensional spaces, Image and video segmentation, Image segmentation, image/video segmentation, Minimum message lengths, Real-world image, Video cameras},

pubstate = {published},

tppubtype = {article}

}