ISSN : 1796-2048
Volume : 3    Issue : 2    Date : June 2008

A Robust Color Image Quantization Algorithm Based on Knowledge Reuse of K-Means
Clustering Ensemble
Yuchou Chang, Dah-Jye Lee, Yi Hong, James Archibald, and Dong Liang
Page(s): 20-27
Full Text:
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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.

Index Terms
color image quantization, clustering ensemble, knowledge reuse, mixture model, expectation
maximization algorithm.