Title :
Non Negative Matrix Factorization Clustering Capabilities; Application on Multivariate Image Segmentation
Author :
Lazar, Cosmin ; Doncescu, Andrei
Author_Institution :
CReSTIC, Univ. of Reims, Reims
Abstract :
The clustering capabilities of the Non Negative Matrix Factorization algorithm is studied. The basis images are considered like the membership degree of the data to a particular class. A hard clustering algorithm is easily derived based on these images. This algorithm is applied on a multivariate image to perform image segmentation. The results are compared with those obtained by Fuzzy K-means algorithm and better clustering performances are found for NMF based clustering. We also show that NMF performs well when we deal with uncorrelated clusters but it cannot distinguish correlated clusters. This is an important drawback when we try to use NMF to perform data clustering.
Keywords :
fuzzy set theory; image segmentation; matrix algebra; pattern clustering; statistical analysis; fuzzy K-means algorithm; fuzzy clustering approach; histogram; multivariate image segmentation; nonnegative matrix factorization clustering algorithm; Application software; Blind source separation; Clouds; Clustering algorithms; Competitive intelligence; Data analysis; Image segmentation; Software algorithms; Software systems; Source separation;
Conference_Titel :
Complex, Intelligent and Software Intensive Systems, 2009. CISIS '09. International Conference on
Conference_Location :
Fukuoka
Print_ISBN :
978-1-4244-3569-2
Electronic_ISBN :
978-0-7695-3575-3
DOI :
10.1109/CISIS.2009.190