Multilevel image thresholding and clustering, two extensively used processing techniques, have sparked renewed interest in recent years due to their wide range of applications. The approach yielding multiple threshold values for each color channel generate clustered segmented images appears be quite efficient it provides significant performance, although this method is computationally heavy. To...

Clustering is the process of dividing a set of input data into a number of subgroups. The members of each subgroup are similar to each other but different from members of other subgroups. The genetic algorithm has enjoyed many applications in clustering data. One of these applications is the clustering of images. The problem with the earlier methods used in clustering images was in selecting in...

Traditional Fuzzy C-means segmentation algorithm requires to set clustering number in advance, and to calculate image clustering center by the iterative arithmetic. So the traditional algorithm is sensitive to the initial value and the computation complexity is high. In order to improve the traditional Fuzzy Cmeans algorithm, this paper presents an infrared image segmentation method using adapt...

In Computer Science, the term segmentation means the process of splitting the digital image into different parts that is the set of pixels. The aim of this paper is to survey the different clustering methods to perform the segmentation in an efficient manner. The clustering method is recommended to carry out the segmentation of an image in a more efficient manner. Clustering can be defined as t...

In this study, an image backlight compensation method using adaptive luminance modification is proposed for efficiently obtaining clear images.The proposed method combines the fuzzy C-means clustering method, a recurrent functional neural fuzzy network (RFNFN), and a modified differential evolution.The proposed RFNFN is based on the two backlight factors that can accurately detect the compensat...

With the advent of the Internet and low-price digital cameras, as well as powerful image editing software, the authenticity of digital images can no longer be taken for granted. Image noises are often introduced into the tampered region during image manipulation process. In this paper, we propose a detection method to locate image forgeries based on noise estimation on HSV color space and hybri...

Clustering is the process of dividing a set of input data into a number of subgroups. The members of each subgroup are similar to each other but different from members of other subgroups. The genetic algorithm has enjoyed many applications in clustering data. One of these applications is the clustering of images. The problem with the earlier methods used in clustering images was in selecting in...

In high dimension space, many conventional clustering algorithms do not work well in effectiveness and efficiency, especially for image data set. For example, k-means is widely used in image clustering especially visual clustering. But its drawback such as long clustering time and high memory cost seriously deteriorates feasibility in incremental large image set. To improve the feasibility, we ...

A key aspect in extracting quantitative information from FMI logs is to segment the FMI image to get image of layers. In this paper, an automatic method based on FCM clustering and Otsu thresholding is introduced in order to extract quantitative information from FMI images. All pixels are clustered using FCM clustering algorithm at the f irst step. The second step uses KNN for other clustering....

Clustering is an unsupervised classification that aims to classify an image into homogeneous regions. We have proposed a hierarchical content based image clustering algorithm to automatically cluster the remote sensing satellite image. The performance evaluation of this algorithm is done with reference to the LISS 4 sensor imagery of IRS-P6 satellite. Centroid of the clusters is uniformly distr...