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Proceedings Paper

Fast image-data classifier
Author(s): D. J. Amalraj; Goutam Dhar
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Paper Abstract

The classification based on the minimum distance classifier has been found to take lesser computing time than any of the maximum likelihood classifiers. An efficient algorithm for classifying image data based on the threshold distance from the 'means' of the classes is presented. The naive algorithm computes the distance of a pixel from every class mean and the pixel is classified to the nearest class. Bryant has reduced the number of computations by first calculating the distance of the pixel from the class, to which the previous pixel was assigned to, and truncating the computation of the distance from subsequent classes whenever the latter is greater. The computation of the distances from subsequent classes is avoided for most cases, in the algorithm presented, by finding the threshold distance for each class, i.e. if the pixel to be classified lies within the threshold distance of the class, it is classified to that class. The minimum of the distances of the class mean from all other class means is calculated. The threshold value of the class is half this minimum. This algorithm is computationally efficient.

Paper Details

Date Published: 1 June 1990
PDF: 6 pages
Proc. SPIE 1244, Image Processing Algorithms and Techniques, (1 June 1990); doi: 10.1117/12.19504
Show Author Affiliations
D. J. Amalraj, Bharat Heavy Electricals Ltd. (India)
Goutam Dhar, Bharat Heavy Electricals Ltd. (India)


Published in SPIE Proceedings Vol. 1244:
Image Processing Algorithms and Techniques
Robert J. Moorhead; Keith S. Pennington, Editor(s)

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