Abstract

Texture is an important visual attribute used to describe images. There are many methods available for texture analysis. However, most of these methods are object to variant rotation and changing scale of the images. Hence, this study presents a novel approach for texture analysis. The approach applies the Particle Swarm Optimization Algorithm in learning the texture filters for texture classifications. In this approach, the texture filter is regarded as the particle; the population of particle is iteratively evaluated according to a statistical performance index corresponding to object classification ability and evolves into the optimal filter using the evolution principles of Particle Swarm Optimization Algorithm. The method has been validated on aerial images and results indicate that proposed method is feasible for texture analysis.

Keywords:

Image segmentation, particle swarm optimization algorithm, texture classification, tuned filter,

References

Competing interests

The authors have no competing interests.

Open Access Policy

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Copyright

The authors have no competing interests.