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Proceedings Paper

Genetic algorithm for texture description and classification
Author(s): Vidya B. Manian; Ramon E. Vasquez
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Paper Abstract

Classification of images requires extraction of optimal set of features. In this paper, a method that uses genetic algorithm creating texture descriptors on features computed from a feature extraction method is presented. A feature extraction algorithm is applied to a database of images and a training feature matrix is created. This matrix is updated by a dynamic algorithm, which finds the vectors most close to the real solution in the Euclidean norm. This set forms the texture descriptor which can be further used for classification of unknown samples. A weighted fitness function that selects best parents in each generation has been implemented. Examples of classification are presented with the features computed from a classification algorithm. Results show that the classification performance of the features improved after applying the genetic algorithm. The algorithm is cost efficient. This algorithm is also compared with that of the Learning Vector Quantization method which quantizes the training vectors to an optimal set of codebook vectors.

Paper Details

Date Published: 30 July 2002
PDF: 7 pages
Proc. SPIE 4736, Visual Information Processing XI, (30 July 2002); doi: 10.1117/12.477592
Show Author Affiliations
Vidya B. Manian, Univ. of Puerto Rico/Mayaguez (United States)
Ramon E. Vasquez, Univ. of Puerto Rico/Mayaguez (United States)

Published in SPIE Proceedings Vol. 4736:
Visual Information Processing XI
Zia-ur Rahman; Robert A. Schowengerdt; Stephen E. Reichenbach, Editor(s)

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