Color image segmentation has been widely applied to diverse fields in the past decades for containing more information than gray ones, whose essence is a process of clustering according to the color of pixels. However, traditional clustering methods do not scale well with the number of data, which limits the ability of handling massive data effectively. We developed an improved kernel clusterin...

In computer vision application, visual features such as shape, color and texture are extracted to characterize images. Each of the features is represented using one or more feature descriptors. One of the important requirements in image retrieval, indexing, classification, clustering, etc. is extracting efficient features from images. The color feature is one of the most widely used visual feat...

Image segmentation is the important task for image analysis and understanding it. The segmentation of color image is challenging task in the image analysis process. Segmentation of color images gives more information than gray scale image. This paper performs segmentation of color image in different color spaces, as segmentation of RGB color image not always gives better segmentation results al...

In this paper, we propose a color image segmentation method based on gravitational clustering using neighbor relations in the spatial domain and distance information in RGB space among pixels. Most clustering-based segmentation algorithms use only color space distances after pixels are mapped from the spatial domain to color space, ignoring their neighbor relations; but we use both information....

K – medoids clustering is used as a tool for clustering color space based on the distance criterion. This paper presents a color image segmentation method which divides colour space into clusters. Through this paper, using various colour images, we will try to prove that K – Medoids converges to approximate the optimal solution based on this criteria theoretically as well as experimentally. Her...

A new color image segmentation scheme is presented in this paper. The proposed algorithm consists of image simplification, region labeling and color clustering. The vector-valued diffusion process is performed in the perceptually uniform LUV color space. We present a discrete 3-D diffusion model for easy implementation. The statistical characteristics of each labeled region are employed to esti...

The color reduction in digital images is an active research area in digital image processing. In many applications such as image segmentation, analysis, compression and transmission, it is preferable to have images with a limited number of colors. In this paper, a color clustering technique which is a combination of a Kohonen Self Organized Featured Map (KSOFM) and a fuzzy clustering algorithm ...

In this paper, an unsupervised segmentation method using clustering is presented for color images. We propose to use a neural network based approach to automatic feature selection to achieve adaptive segmentation of color images. With a self-organizing feature map (SOFM), multiple color features can be analyzed, and the useful feature sequence (feature vector) can then be determined. The encode...

We demonstrate interactive visual embedding of partition-based clustering of multidimensional data using methods from the open-source machine learning library Weka. According to the visual analytics paradigm, knowledge is gradually built and refined by a human analyst through iterative application of clustering with different parameter settings and to different data subsets. To show clustering ...

Wepresentmeasurements of the color and luminosity dependence of galaxy clustering at z 1 in theDEEP2Galaxy Redshift Survey. Using volume-limited subsamples in bins of both color and luminosity, we find the following: (1) The clustering dependence is much stronger with color than with luminosity and is as strong with color at z 1 as is found locally. We find no dependence of the clustering ampli...