Today I read a paper titled “Fast Color Quantization Using Weighted Sort-Means Clustering”
The abstract is:
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.