Genetic algorithm for texture description and classification

Vidya B. Manian; Ramon E. Vasquez

doi:10.1117/12.477592

30 July 2002 Genetic algorithm for texture description and classification

Vidya B. Manian, Ramon E. Vasquez

Proceedings Volume 4736, Visual Information Processing XI; (2002) https://doi.org/10.1117/12.477592
Event: AeroSense 2002, 2002, Orlando, FL, United States

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.

Citation Download Citation

Vidya B. Manian and Ramon E. Vasquez "Genetic algorithm for texture description and classification", Proc. SPIE 4736, Visual Information Processing XI, (30 July 2002); https://doi.org/10.1117/12.477592

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KEYWORDS

Genetic algorithms

Image classification

Feature extraction

Evolutionary algorithms

Quantization

Organisms

Wavelets

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