Algorithms for image classification using wavelets and fusion methods

This work presents a set of algorithms for feature extraction and image classification using wavelet transform and fusion methods. First we use wavelet transform as a feature extraction method. We present some basic definitions and theoretical background of the problem and proposed methodologies for solving it.; We decompose the image to a second level decomposition giving us the coefficients of approximation, diagonal, horizontal and vertical. We used nine different wavelet filters in order to test how the classification performed depending on the filters, images and coefficients used. The algorithms used for image classification are Euclidean Distance, Maximum Likelihood and K-Nearest Neighbor. Then all of the results from the algorithms individually go to a fusion center where we use Majority Voting.; Images from the Brodatz album and an image of Mayaguez Bay are used. Different experiments were done trying to aim at the best results possible. We got really interesting results for some of the textures. Some of them gave us really good results for the classifiers and filters, but the others did not. We normalized all of the samples in order to achieve a better discrimination.