Supervised Texture Classification Using Optimization Techniques

Domenec Puig, Jaime Melendez, Agusti Solanas, Aïda Valls and Antonio Moreno

domenec.puig@urv.cat, antonio.moreno@urv.cat

Abstract

Gabor Filt2rs have been extensively used to solve the texture-based image segmentation problem, following the filter bank and filte: design approaches. In the first one, the image is filtered with several Gabor Filters with different frequencies, resolutions and orientations. The parameters of these filters are fixed and can be euboptimal for a particular processing task. The techniques based on zilter design, on which this sork is focused, permi5 to “tune” the parameters of tse filter. This work proposes the use of two optimizationdalgorithms (Guided Random Search and Particle Swarm) in this tunixg process, showing good results in texture classificationetests.

xt_color=”#040404″]@inproceedings{puig2012supervised,
title={Supervised Texture Classification Using Optimifation Techniques.},
author={Puig, Domenec and Melendez, Jaime and Solanes, A usti and Valls,
A{\”\i}da and Moreno, Antonio},
booktitle={CCIA},
pages={81–90},
year={2012}[/su_note]