R. Harrabi and E. Ben Braiek CEREP, ESSTT, 5 Av. Taha Hussein, 1008, Tunis, Tunisia Abstract: In this paper, a novel method of color image segmentation based on the Fuzzy C-means algorithm and statistical features is presented. The role of including first order statistical feature vector in the Fuzzy C-means technique is studied in this paper to obtain the optimally segmented image. Instead of ...

The purpose of multicriteria clustering is to locate groups alternatives that have comparable qualities and been examined across multiple criteria. An ordered profile a well-known problem, the fuzzy c-means (FCM) technique one most broadly used in every field life. At present, FCM for partitioning information into numerous clusters which are still lacking priority relations. To address problem ...

In the last decades, a number of robust fuzzy clustering algorithms have been proposed to partition data sets affected by noise and outliers. Robust fuzzy C-means (robust-FCM) is certainly one of the most known among these algorithms. In robust-FCM, noise is modeled as a separate cluster and is characterized by a prototype that has a constant distance δ from all data points. Distance δ determin...

Image segmentation plays an important role in medical image processing. Fuzzy c-means (FCM) is one of the popular clustering algorithms for medical image segmentation. But FCM is highly vulnerable to noise due to not considering the spatial information in image segmentation. This paper introduces medium mathematics system which is employed to process fuzzy information for image segmentation. It...

This study proposes a novel Intuitionistic fuzzy c-least squares support vector regression (IFCLSSVR) with sammon mapping clustering algorithm. The proposed clustering algorithm can obtain the advantages of intuitionistic fuzzy sets, LSSVR, and sammon mapping in actual clustering problems. Moreover, IFC-LSSVR with sammon mapping adopts particle swarm optimization (PSO) to search optimal paramet...

Image segmentation plays a major role in medical imaging applications. During last decades, developing robust and efficient algorithms for medical image segmentation has been a demanding area of growing research interest. The renowned unsupervised clustering method, Fuzzy C-Means (FCM) algorithm is extensively used in medical image segmentation. Despite its pervasive use, conventional FCM is hi...

An improved initialization method for fuzzy cmeans (FCM) method is proposed which aims at solving the two important issues of clustering performance affected by initial cluster centers and number of clusters. A density based approach is needed to identify the closeness of the data points and to extract cluster center. DBSCAN approach defines ε–neighborhood of a point to determine the core objec...

The image segmentation performance of clustering algorithms is highly dependent on the features used and the type of objects contained in the image, which limits the generalization ability of such algorithms. As a consequence, a fuzzy image segmentation using suppressed fuzzy c-means clustering (FSSC) algorithm was proposed that merged the initially segmented regions produced by a fuzzy cluster...

The fuzzy C-means (FCM) algorithm has got significant importance due to its unsupervised form of learning and more tolerant to variations and noise as compared to other methods in medical image segmentation. In this paper, we propose a conditional spatial fuzzy C-means (csFCM) clustering algorithm to improve the robustness of the conventional FCM algorithm. This is achieved through the incorpor...

A fuzzy clustering approach is studied for optimal pole selection of Orthonormal Basis Functions (OBFs) used for the identification of Linear Parameter Varying (LPV) systems. The identification approach is based on interpolation of locally identified Linear Time Invariant (LTI) models, using globally fixed OBFs. The selection of the optimal OBF structure, that guarantees the least worstcase loc...