Title :
A novel robust and fast segmentation of the color images using fuzzy classification c-means
Author :
Toure, Mohamed Lamine ; Beiji, Zou ; Musau, Felix
Author_Institution :
Sch. of Inf. Sci. & Eng., Central South Univ., Changsha, China
Abstract :
This 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 c-means (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.
Keywords :
image colour analysis; image segmentation; pattern clustering; color image segmentation; fuzzy c-means clustering; fuzzy classification c-means; iterative partitioning method; optimal c-partitions; Clustering algorithms; Color; Fuzzy sets; Image segmentation; Iterative algorithms; Iterative methods; Partitioning algorithms; Robustness; System performance; Testing; Classification; FCM; FuzzyLogic; Merge regions; Optimal c-partitions; Segmentation;
Conference_Titel :
Education Technology and Computer (ICETC), 2010 2nd International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-6367-1
DOI :
10.1109/ICETC.2010.5529667
