A Robust Color Image Quantization Algorithm Based on Knowledge Reuse of K-Means Clustering Ensemble

Abstract

This paper presents a novel color image quantization algorithm. This algorithm improves color image quantization stability and accuracy using clustering ensemble. In our approach, we firstly adopt manifold single k-means clusterings for the color image to form a preliminary ensemble committee. Then, in order to avoid inexplicit correspondence among clustering groups, we use the original color values of each clustering centroid directly to construct a final ensemble committee. A mixture model based on the expectation-maximization (EM) algorithm is used as a consensus function to combine the clustering groups of the final ensemble committee to obtain color quantization results. Experimental results reveal that the proposed color quantization algorithm is more stable and accurate than k-means clustering. The preprocessing step of the algorithm, k-means clustering, can be implemented and executed in parallel to improve processing speed.