On combining Learning Vector Quantization and the Bayesian classifiers for natural textured images

One objective for classifying textures in natural images is to achieve the best performance possible. Unsupervised techniques are suitable when no prior knowledge about the image content is available. The main drawback of unsupervised approaches is its worst performance as compared against supervised ones. We propose a new unsupervised hybrid approach based on two welltested classifiers: Vector Quantization (VQ) and Bayesian (BY). The VQ unsupervised method establishes an initial partition which is validated and improved through the supervised BY. A comparative analysis is carried out against classical classifiers, verifying its performance.