Fuzzy Clustering Image Segmentation Based on Particle Swarm Optimization
نویسندگان
چکیده
Image segmentation refers to the technology to segment the image into different regions with different characteristics and to extract useful objectives, and it is a key step from image processing to image analysis. Based on the comprehensive study of image segmentation technology, this paper analyzes the advantages and disadvantages of the existing fuzzy clustering algorithms; integrates the particle swarm optimization (PSO) with the characteristics of global optimization and rapid convergence and fuzzy clustering (FC) algorithm with fuzzy clustering effects starting from the perspective of particle swarm and fuzzy membership restrictions and gets a PSO-FC image segmentation algorithm so as to effectively avoid being trapped into the local optimum and improve the stability and reliability of clustering algorithm. The experimental results show that this new PSO-FC algorithm has excellent image segmentation effects.
منابع مشابه
Particle Swarm Optimization Based Spatial Credibilistic Clustering Algorithm Applied in High Noise Image Segmentation
In practice, noise images even high noise images are very common. It’s very essential and critical to deal with such kind of images to process real-image segmentation and pattern recognition. In this paper, differences of credibilistic clustering algorithm (CCA) and fuzzy c-means algorithm (FCM) in dealing with noise images are studied and the research shows that in most case, CCA performs bett...
متن کاملModified CLPSO-based fuzzy classification System: Color Image Segmentation
Fuzzy segmentation is an effective way of segmenting out objects in images containing both random noise and varying illumination. In this paper, a modified method based on the Comprehensive Learning Particle Swarm Optimization (CLPSO) is proposed for pixel classification in HSI color space by selecting a fuzzy classification system with minimum number of fuzzy rules and minimum number of incorr...
متن کاملA image segmentation algorithm based on differential evolution particle swarm optimization fuzzy c-means clustering
This paper presents a hybrid differential evolution, particle swarm optimization and fuzzy c-means clustering algorithm called DEPSO-FCM for image segmentation. By the use of the differential evolution (DE) algorithm and particle swarm optimization to solve the FCM image segmentation influenced by the initial cluster centers and easily into a local optimum. Empirical results show that the propo...
متن کاملHybridization of Particle Swarm Optimization with Unsupervised Clustering Algorithms for Image Segmentation
Unsupervised fuzzy clustering algorithms are one of many approaches used in image segmentation. The Fuzzy C-means algorithm (FCM) and the Possibilistic C-means algorithm (PCA) have been widely used. There is also the generalized possibilistic algorithm (GPCA). GPCA was proposed recently and is a general form of the previous algorithms. These clustering algorithms can be trapped to the local opt...
متن کاملMR Brain Image Segmentation Using an Improved Kernel Fuzzy Local Information C-Means Based Wavelet, Particle Swarm Optimization (PSO) Initialization and Outlier Rejection with Level Set Methods
This paper, presents a new image segmentation method based on Wavelets, Particle Swarm Optimization (PSO) and outlier rejection caused by the membership function of the kernel fuzzy local information c-means (KFLICM) algorithm combined with level set is proposed. The segmentation of Magnetic Resonance (MR) images plays an important role in the computer-aided diagnosis and clinical research, but...
متن کامل