Fast color reduction using approximative c-means clustering models

In this paper we propose an efficient color reduction framework that employs c-means clustering to extract optimal colors. The processing consists of three stages: preprocessing, c-means clustering, and creation of the output image. The main goal of the first stage is to transform the pixel matrix into a list of records, which indicates what colors are present in the image and how many times they appear. To achieve this, first we apply a static color quantization scheme that aligns the 16.7 million possible colors with 140 thousand grid points, and build the histogram of this quantized image. Then we mark least frequent quantized colors to be ignored during the clustering stage, the amount of such marks being controlled by the pixel inclusion parameter. Leaving out 2-5% of the image pixels can reduce the number of colors to 500-5000 in most images. This limited set of colors together with frequency information consists the input of the c-means clustering process performed in the second stage. Before creating the final output image, the marked quantized colors are mapped to the closest cluster. Thorough numerical tests were performed on 500 randomly chosen images using both fuzzy and hard c-means clustering. Evaluations revealed that hard c-means is more suitable than fuzzy c-means for the given problem, both in terms of accuracy and efficiency. The proposed method performs quicker 2-3 times than other recent reported solutions.