Fast algorithm for GMM-based pattern classifier
Shogo Muramatsu, Hidenori Watanabe · 2009
This work proposes a fast decision algorithm in pattern classification based on Gaussian mixture models (GMM). Statistical pattern classification problems often meet a situation that comparison between probabilities is obvious and involve redundant computations. When GMM is adopted for the probability model, the exponential function should be evaluated. This work firstly reduces the exponential computations to simple and rough interval calculations. The exponential function is realized by scaling and multiplication with powers of two so that the decision is efficiently realized. For finer decision, a refinement process is also proposed. In order to verify the significance, experimental results on TI DM6437 EVM board are shown through the application to a skin-color extraction problem. It is verified that the classification was almost completed without any refinement process and the refinement process can proceed the residual decisions.