Color Quantization of still images can be easily stated as a Clustering problem. Color Quantization of sequences of images becomes a Non-stationary Clustering Problem. In this paper we propose a very simple and e€ective Evolution-based Adaptive Strategy to perform the adaptive computation of the color representatives for each image in the sequence. Salient features of the algorithm proposed her...

An hybrid segmentation method for color images is presented in this work. It combines 2D histogram clustering to produce segmentation maps fused together providing an initial unsupervised clustering of the dominant colors of the image. Region information is then used and a novel technique is introduced to simplify the Region Adjacency Graph by merging candidate regions until the stabilization o...

In this paper, we present the application of the fuzzy c-means clustering algorithm to the skin-color segmentation problem. We address the problem of identifying skin-color and we adapt a spatial data mining method to this task and integrate with a segmentation method to identify significant skin-color regions in an image. The proposed algorithm is able to take into account both the distributio...

We introduce a multi-feature optimization clustering algorithm for color image segmentation. The local binary pattern, the mean of the min-max difference, and the color components are combined as feature vectors to describe the magnitude change of grey value and the contrastive information of neighbor pixels. In clustering stage, it gets the initial clustering center and avoids getting into loc...

Segmentation of an image entails the division or separation of the image into regions of similar attribute. The most basic attribute for segmentation of an image is its luminance amplitude for a monochrome image and color components for a color image. The main objective of this paper is to segment the natural crops images by using clustering techniques which is produced very good results. The c...

This paper presents a new method for color image segmentation based on a scale-space clustering of the image pixels. Unlike standard image scale-spaces, which smooth the images, this approach consider an augmented space which combines spatial and color dimensions. The clustering relies on the mean-shift algorithm to find the modes of the position and color distribution of the pixels. In order t...

An hybrid segmentation method for color images is presented in this work. It combines 2D histogram clustering to produce segmentation maps fused together providing an initial unsupervised clustering of the dominant colors of the image. Region information is then used and a novel technique is introduced to simplify the Region Adjacency Graph by merging candidate regions until the stabilization o...

Color quantization of still images can be easily stated as a clustering problem. Color quantization of sequences of images becomes a non-stationary Clustering problem. In this paper we propose a very simple and effective evolutive strategy to perform adaptively the computation of the color representatives for each image in the sequence. Salient features of the evolutive strategy proposed here a...

We study a combinatorial problem, called token clustering, on a hypercube: given N colored tokens (k tokens per color) each one placed on one processor of the N-node hypercube, cluster all the tokens with the same color into the same k-node subcube. This problem has natural applications in several areas. We propose a distributed algorithm to solve the clustering problem on hypercubes and show s...

A clustering-based lip segmentation algorithm is described here. The fuzzy clustering algorithm presented here is able to take into account the local smoothness property of image data. The objective functional of our algorithm utilizes a new distance metric that takes into account the influence of the neighboring pixels on the centre pixel in a 3 by 3 window. Computational steps involved in the...