Fast Color Quantization Using Weighted Sort-Means Clustering

Color quantization is an important operation with numerous applications in graphics and image processing. Most quantization methods are essentially based on data clustering algorithms. However, despite its popularity as a general purpose clustering algorithm, k-means has not received much respect in the color quantization literature because of its high computational requirements and sensitivity to initialization. In this paper, a fast color quantization method based on k-means is presented. The method involves several modifications to the conventional (batch) k-means algorithm including data reduction, sample weighting, and the use of triangle inequality to speed up the nearest neighbor search. Experiments on a diverse set of images demonstrate that, with the proposed modifications, k-means becomes very competitive with state-of-the-art color quantization methods in terms of both effectiveness and efficiency.Comment: 30 pages, 2 figures, 4 table

Color Reduction in Hand-drawn Persian Carpet Cartoons before Discretization using image segmentation and finding edgy regions

In this paper, we present a method for color reduction of Persian carpet cartoons that increases both speed and accuracy of editing. Carpet cartoons are in two categories: machine-printed and hand-drawn. Hand-drawn cartoons are divided into two groups: before and after discretization. The purpose of this study is color reduction of hand-drawn cartoons before discretization. The proposed algorithm consists of the following steps: image segmentation, finding the color of each region, color reduction around the edges and final color reduction with C-means. The proposed method requires knowing the desired number of colors in any cartoon. In this method, the number of colors is not reduced to more than about 1.3 times of the desired number. Automatic color reduction is done in such a way that final manual editing to reach the desired colors is very easy

Improving the Performance of K-Means for Color Quantization

Color quantization is an important operation with many applications in graphics and image processing. Most quantization methods are essentially based on data clustering algorithms. However, despite its popularity as a general purpose clustering algorithm, k-means has not received much respect in the color quantization literature because of its high computational requirements and sensitivity to initialization. In this paper, we investigate the performance of k-means as a color quantizer. We implement fast and exact variants of k-means with several initialization schemes and then compare the resulting quantizers to some of the most popular quantizers in the literature. Experiments on a diverse set of images demonstrate that an efficient implementation of k-means with an appropriate initialization strategy can in fact serve as a very effective color quantizer.Comment: 26 pages, 4 figures, 13 table

Analytical methods fort he study of color in digital images

La descripció qualitativa dels colors que composen una imatge digital és una tasca molt senzilla pel sistema visual humà. Per un ordinador aquesta tasca involucra una gran quantitat de qüestions i de dades que la converteixen en una operació de gran complexitat. En aquesta tesi desenvolupam un mètode automàtic per a la construcció d’una paleta de colors d’una imatge digital, intentant respondre a les diferents qüestions que se’ns plantegen quan treballam amb colors a dins el món computacional. El desenvolupament d’aquest mètode suposa l’obtenció d’un algorisme automàtic de segmentació d’histogrames, el qual és construït en detall a la tesi i diferents aplicacions del mateix son donades. Finalment, també s’explica el funcionament de CProcess, un ‘software’ amigable desenvolupat per a la fàcil comprensió del color