A novel fuzzy clustering algorithm with non local adaptive spatial constraint for image segmentation

Semi-supervising Interval Type-2 Fuzzy C-Means clustering with spatial information for multi-spectral satellite image classification and change detection

“…In order to improve the quality of clustering in image segmentation including satellite images, various ways of using spatial information together with color information in FCM algorithm were proposed (Despotovic et al, 2010;Wang et al, 2009Wang et al, , 2013Zhao et al, 2011Zhao et al, , 2013Liu and Pham, 2012;Zhao, 2013;Vargas et al, 2013;Benaichouche et al, 2013). Despotovic et al (2010) used a mask whose center is the considered pixel, while relationships between pixels in the mask and the center are used to determine the degree of similarity being used to estimate the membership values.…”

An Optimization Clustering Algorithm Based on Texture Feature Fusion for Color Image Segmentation

“…represents represent the clustering center of the color component which is the color feature. 4 represents the clustering centre of LBP code. 5 represents the clustering center of the mean of difference in equation (14).…”

“…The Rank M-type L (RM-L) and L-estimators are used to obtain the sufficient spatial information of the pixels [3]. A robust modified FCM is presented by introducing a non-local adaptive spatial constraint term into the objective function [4]. An algorithm for fuzzy segmentation of MRI data by using two fuzzifiers used in interval type-2 FCM and a spatial constraint on the membership functions is present [5].…”

Robust c-prototypes algorithms for color image segmentation

“…In general, the FCM algorithm is a highly effective methodology to segment noise-free images, but in the presence of natural artifacts (noise, intensity, or color inhomogeneity in the regions, the regions with similar textures, shadows, object reflections, etc. ), the FCM has two shortcomings that make it very sensitive: in its conventional nomenclature, it does not consider any spatial information in the image context [4][5][6][7][8], and the second one is that the objective function can be seen as a formulation of the least squares method, in which one tries to minimize the error between the feature vector and the vector with the centers of the groups. Outliers have a great effect during minimization since there is a quadratic function in the objective function of the FCM algorithm; so, it is necessary to use a quadratic function with the property of being less increasing, and thereby control the influence of atypical information.…”

“…Other authors such as Zhang and Chen [6] follow the same work of Ahmed et al but their algorithms introduced the median-and mean-filtered images, which can be computed in advance and hence, can reduce the computation time. Zhao et al [7] used an adaptive spatial parameter for each pixel that was designed to make the non-local spatial information of each pixel playing a different role to guide the noisy image segmentation. Liu and Pham [8] modified the FCM by adding a spatial penalty term into the objective function.…”