Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Skin Segmentation Using Color Pixel Classification: Analysis and Comparison
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
An efficient digital VLSI implementation of Gaussian mixture models-based classifier
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Numerical Recipes 3rd Edition: The Art of Scientific Computing
Numerical Recipes 3rd Edition: The Art of Scientific Computing
Digital Signal Processing with Field Programmable Gate Arrays
Digital Signal Processing with Field Programmable Gate Arrays
A Study of Variable-Parameter Gaussian Mixture Hidden Markov Modeling for Noisy Speech Recognition
IEEE Transactions on Audio, Speech, and Language Processing
Variational learning for Gaussian mixture models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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This paper 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 and TED TB-3S-3400DSP-IMG board are shown through the application to a 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.