Filter selection using genetic algorithms

Convolution operators act as matched filters for certain types of variations found in images and have been extensively used in the analysis of images. However, filtering through a bank of N filters generates N filtered images, consequently increasing the amount of data considerably. Moreover, not all these filters have the same discriminatory capabilities for the individual images, thus making the task of any classifier difficult. In this paper, we use genetic algorithms to select a subset of relevant filters. Genetic algorithms represent a class of adaptive search techniques where the processes are similar to natural selection of biological evolution. The steady state model (GENITOR) has been used in this paper. The reduction of filters improves the performance of the classifier (which in this paper is the multi-layer perceptron neural network) and furthermore reduces the computational requirement. In this study we use the Laws filters which were proposed for the analysis of texture images. Our aim is to recognize the different textures on the images using the reduced filter set.