Abstract
In this article, Winograd's method is used with Euclidean distance, Mahalanobis distance and Maximum Likelihood classifiers to reduce their computational time requirements. Experimental work is carried out with 6 band thematic mapper data. The proposed fast algorithms for Euclidean and Maximum Likelihood classifiers are observed to be 2 times faster than their literal algorithms. In the case of the Mahalanobis distance classifier, the proposed fast algorithm is showing a speed-up of 7. Use of this logic with other pattern recognition algorithms is also discussed.
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