A novel robust and fast Segmentation of the Color Images using Fuzzy Classification C-means

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dc.contributor.authorFelix Musau
dc.contributor.authorMohamed Lamine Toure
dc.contributor.authorZou Beiji
dc.date.accessioned2024-08-14T07:03:58Z
dc.date.available2024-08-14T07:03:58Z
dc.date.issued2010
dc.description.abstractThis paper brings out a method for segmentation of color images based on fuzzy classification. It proceeds in a first step by a fine segmentation using the algorithm of fuzzy cmeans (FCM). The method then applies a test fusion of fuzzy classes. The result is a coarse segmentation, where each region is the union of elementary regions grown from FCM. The fuzzy C-Means (FCM) clustering is an iterative partitioning method that produces optimal c-partitions, the standard FCM algorithm takes a long time to partition a large data set. The proposed FCM program must read the entire data set into a memory for processing. Our results show that the system performance is robust to different types of images.
dc.identifier.citationToure, M. L., Beiji, Z., & Musau, F. (2010). A novel robust and fast Segmentation of the Color Images using Fuzzy Classification C-means. In 2nd International Conforence on Education Technology and Computer (ICETC), pp. V4-341-V4-344. https://doi: 10.1109/ICETC.2010.5529667.
dc.identifier.urihttps://repository.ru.ac.ke/handle/123456789/53
dc.language.isoen
dc.publisherIEEE
dc.relation.ispartofseries2nd International Conforence on Education Technology and Computer (ICETC)
dc.subjectSegmentation
dc.subjectClassification
dc.subjectFUZlJ'Logic
dc.subjectFCM
dc.subjectMerge regions
dc.subjectOptimal c-partitions.
dc.titleA novel robust and fast Segmentation of the Color Images using Fuzzy Classification C-means
dc.typeArticle

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