Fuzzy Image Segmentation using Suppressed Fuzzy C-Means Clustering

Clustering algorithms are highly dependent on the features used and the type of the objects in a particular image. By considering object similar surface variations (SSV) as well as the arbitrariness of the fuzzy c-means (FCM) algorithm for pixellocation, a fuzzy image segmentation considering object surface similarity (FSOS) algorithm was developed, but it was unable to segment objects having SSV satisfactorily. To improve the effectiveness of FSOS in segmenting objects with SSV, thispaper introduces a new fuzzy image segmentation using suppressed fuzzy c-means clustering (FSSC) algorithm, which directly considers object SSV and incorporates the use of suppressed-FCM (SFCM) using pixel location. The algorithmalso perceptually selects the threshold within the range of human visual perception. Both qualitative and quantitative resultsconfirm the improved segmentation performance of FSSC compared with other algorithms including FSOS, FCM,possibilistic c-means (PCM) and SFCM for many different images

Fuzzy C-Means Clustering Based on Improved Marked Watershed Transformation

Currently, the fuzzy c-means algorithm plays a certain role in remote sensing image classification. However, it is easy to fall into local optimal solution, which leads to poor classification. In order to improve the accuracy of classification, this paper, based on the improved marked watershed segmentation, puts forward a fuzzy c-means clustering optimization algorithm. Because the watershed segmentation and fuzzy c-means clustering are sensitive to the noise of the image, this paper uses the adaptive median filtering algorithm to eliminate the noise information. During this process, the classification numbers and initial cluster centers of fuzzy c-means are determined by the result of the fuzzy similar relation clustering. Through a series of comparative simulation experiments, the results show that the method proposed in this paper is more accurate than the ISODATA method, and it is a feasible training method

Attribute Identification and Predictive Customisation Using Fuzzy Clustering and Genetic Search for Industry 4.0 Environments

Today´s factory involves more services and customisation. A paradigm shift is towards “Industry 4.0” (i4) aiming at realising mass customisation at a mass production cost. However, there is a lack of tools for customer informatics. This paper addresses this issue and develops a predictive analytics framework integrating big data analysis and business informatics, using Computational Intelligence (CI). In particular, a fuzzy c-means is used for pattern recognition, as well as managing relevant big data for feeding potential customer needs and wants for improved productivity at the design stage for customised mass production. The selection of patterns from big data is performed using a genetic algorithm with fuzzy c-means, which helps with clustering and selection of optimal attributes. The case study shows that fuzzy c-means are able to assign new clusters with growing knowledge of customer needs and wants. The dataset has three types of entities: specification of various characteristics, assigned insurance risk rating, and normalised losses in use compared with other cars. The fuzzy c-means tool offers a number of features suitable for smart designs for an i4 environment

Improved Method for Fuzzy C-Means Clustering Algorithm

由于传统的模糊C均值(fuzzy C-MEAnS,fCM)算法没有考虑像素点的空间邻域信息,仅涉及像素的单点灰度,在处理含有噪声的图像时有很大的局限性,因此分割效果较差。针对fCM的缺陷,提出一种新的改进算法,该算法引入gIbbS随机场,将gIbbS随机场先验概率与像素点隶属度的乘积作为新的像素隶属度。实验表明,改进后的算法有良好的分割效果,既可以较为完整地保留图像边界细节,又能较好地去除图像的噪声。The traditional fuzzy C-means (FCM) algorithm has great limitations in dealing with the noisy images owing to not considering the spatial information of the pixels and only involving the pixel gray of a single point,so it's poor in segmenting an image.For the defects of the FCM algorithm,a new improved algorithm is proposed in this article,in which a product of Gibbs priori probability and the membership is regarded as the new pixel membership.Experimental results show that the improved algorithm has a good segmentation result;it can retain more complete edge details of image and can remove the image noise more effectively.南京军区重点项目(项目编号:11Z023); 福建省自然科学基金项目(项目编号:2008J0312

Cluster Analysis Based on Bipartite Network

Clustering data has a wide range of applications and has attracted considerable attention in data mining and artificial intelligence. However it is difficult to find a set of clusters that best fits natural partitions without any class information. In this paper, a method for detecting the optimal cluster number is proposed. The optimal cluster number can be obtained by the proposal, while partitioning the data into clusters by FCM (Fuzzy c-means) algorithm. It overcomes the drawback of FCM algorithm which needs to define the cluster number c in advance. The method works by converting the fuzzy cluster result into a weighted bipartite network and then the optimal cluster number can be detected by the improved bipartite modularity. The experimental results on artificial and real data sets show the validity of the proposed method