The mixtures of Student's t-distributions as a robust framework for rigid registration
Image and Vision Computing
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This paper concerns a greedy EM algorithm for t-mixture modeling, which is more robust than Gaussian mixture modeling when atypical points exist or the set of data has heavy tail. Local Kullback divergence is used to determine how to insert new component. The greedy algorithm obviates the complicated initialization. The results are comparable to that of Split-and-Merge EM algorithm while the proposed algorithm is faster. Also the byproduct of a sequence of mixture models is useful for model selection. Experiments of synthetic data clustering and unsupervised color image segmentation are given.