Abstract

Abstract — Color quantization is an important operation with many applications in graphics and image processing. Most quantization methods are essentially based on data clustering algorithms one which is the popular k-means algorithm. A common drawback of many conventional clustering algorithms is the generation of empty clusters (dead units). In this paper, we apply Uchiyama and Arbib’s competitive learning algorithm [1] to the problem of color quantization. In contrast to the conventional batch k-means algorithm, this competitive learning algorithm requires no cluster center initialization. In addition, it effectively avoids the dead unit problem by utilizing a simple cluster splitting rule. Experiments on commonly used test images demonstrate that the presented method outperforms various stateof-the-art methods in terms of quantization effectiveness.

Citations