Color image segmentation is an important task for computer vision. The segmented RGB color space is not more reliable and accurate for computer vision applications. For this purpose, the proposed approach combines different color spaces such as RGB, HSV, YIQ and XYZ for image segmentation. The combine segmentation of various color spaces to give more accurate segmentation result compared to seg...

This paper presents a semi automatic method used to segment color documents into different uniform color plans. The practical application is dedicated to administrative documents segmentation. In these documents, like in many other cases, color has a semantic meaning: it is then possible to identify some specific regions like manual annotations, rubber stamps or colored highlighting. A first st...

Color image quantization, the process of reducing the number of colors in a digital color image, has been widely studied for the last fifteen years. The different steps of clustering methods are studied. The methods are compared step by step and some optimizations of the algorithms and data structures are given. A new color space called H1H2H3 is introduced, which improves the quantization heur...

A novel approach to the extraction of image regions of uniform color and its application to automatic texture detection is discussed. The method searches for alternating color patterns, through hierarchical clustering of color pairs from adjacent image regions. The final result is a hierarchy of texture regions, described by their boundaries and a set of features, detected at multiple accuracy ...

We argue that clustering of color sources, leading to the percolation transition, may be the way to achieve deconfinement in heavy ion collisions. The critical density for percolation is related to the effective critical temperature of the thermal bath asociated to the presence of strong color fields inside the percolating cluster. We find that the temperature is rapidity, centrality and energy...

We propose a video retrieval framework based on a novel combination of spatiograms and the Jensen-Shannon divergence, and validate its performance in two quantitative experiments on TRECVID BBC Rushes data. In the first experiment, color-based methods are tested by grouping redundant shots in an unsupervised clustering. Results of the second experiment show that motion-based spatiograms make a ...

Color image segmentation plays an important role in computer vision and image processing areas. In this paper, the features of Volterra filter are utilized for color image segmentation. The discrete Volterra filter exhibits both linear and nonlinear characteristics. The linear part smoothes the image features in uniform gray zones and is used for getting a gross representation of objects of int...

This paper presents a new approach to hierarchical clustering of point patterns. Two algorithms for hierarchical locationand densitybased clustering are developed. Each method groups points such that maximum intracluster similarity and intercluster dissimilarity are achieved for point locations or point separations. Performance of the clustering methods is compared with four other methods. The ...

Color is the major source of information widely used in image analysis and content-based retrieval. Extracting dominant colors that are prominent in a visual scenery is of utmost importance since the human visual system primarily uses them for perception and similarity judgment. In this paper, we address dominant color extraction as a dynamic clustering problem and use techniques based on Parti...

Color quantization is a process of sampling three-dimensional color space (e.g. RGB) to reduce the number of colors in a color image. By reducing to a discrete subset of colors known as a color codebook or palette, each pixel in the original image is mapped to an entry according to these palette colors. In this paper, a reinforcement-learning approach to color image quantization is proposed. Fu...