## 1 Introduction  
Image segmentation is a process of dividing an image into different regions such that each item in the same class is as similar as possible whereas items in different classes are as dissimilar as possible. It is a key in image analysis and pattern recognition. However, because of the variety and complexity of images, image segmentation is still a very challenging task.  
Fuzzy c-means (FCM) algorithm is one of the most widely used methods for color image segmentation. However, the selection of the initial parameters of standard FCM usually influences the clustering results greatly. Moreover, it does not consider any spatial information in image context, which makes it very sensitive to noise. To minimize the disadvantages of standard FCM, various modified FCM algorithms have been proposed. A comprehensive comparative analysis of kernel-based fuzzy clustering and fuzzy clustering is presented [1,2]. It introduces enhanced FCM clustering algorithms with spatial constraints for noisy color image segmentation. 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].  
Most of the above methods are dependent on separate features to complete segmentation. If inadequate features are used, even the best classifier could fail to achieve accurate segmentation. So it is important that how to effectively choose image features. Recently, the local binary pattern (LBP) operator has received considerable attention. It has been successfully applied to computer vision tasks, such as texture classification [6], face recognition [7, 8, 9], object detection [10,11], and fingerprint matching [12]. It is a non-parametric kernel which summarizes the local structure around a pixel and provides a unified description, including statistical and structural characteristics of a texture patch.  
Particle swarm optimization (PSO) has been suggested by [PERSON] and [PERSON] [13]. It is inspired by certain social behavior of bird flocking or fish schooling. When used for solving the optimization problem, PSO makes every particle fly through the solution space with a certain trajectory. Under the guidance of its own or its neighbor's experience, each particle will gradually fly into the potential area of global optimum [14]. PSO is gaining more and more attention due to its fast convergence rate, simple algorithmic structure and strong global optimization capability. It has been applied to solve varieties of optimization problems successfully [15, 16, 17].  
In this paper, we introduce an improved FCM segmentation algorithm for color images. We choose LBP, the mean of the min-max difference, and color features as a segmental feature vector. It is a more accurate description of multi-feature. In the clustering stage, we use an improved PSO method to optimize the initial clustering centers. PSO is used to ensure that the search converges faster, and we add mutation operator into PSO to make the search jump out of local optima. Subsequently, we use FCM algorithm to complete color image segmentation. The experimental results show that the proposed method has a better segmentation performance.  
The rest of the paper is organized as follows. Section 2 introduces LBP operator and analyzes the drawbacks of LBP operator. Section 3 describes the PSO algorithm. Section 4 presents the proposed method. Section 5 compares the proposed algorithm with some existing methods. Section 6 gives concluding remarks.