Fast Gaussian Mixture Clustering for Skin Detection

EM is one of the popular algorithms which can be applied to skin segmentation. Due to the high computational cost of EM, traditional EM is difficult to apply to a large skin database. Inspired by the idea of subsampling, we integrate EM with incremental clustering and hierarchical clustering to estimate the parameters of mixture models. The algorithm first selects the samples by the incremental clustering approach and hierarchical clustering approach. Then, EM is applied to the sample set. The experiments show that the new EM algorithm works well in the skin database.

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