Texture classification using Steerable Pyramid based Laws' Masks

This paper progress towards a new feature extraction technique by combining the two existing methods named as Laws' mask and steerable pyramid for texture classification. Texture parameters are derived and classified for accepted Laws' mask method. In this paper texture features are extracted and classified using new approaches, which are carried out by integrating both steerable pyramid and Laws' mask (SPLM) methods. The comparison of the methods yields that the Steerable Pyramid based Laws' Mask (SPLM) texture feature extraction technique using fifth level of image decomposition level resulted in the best classification accuracy. We use simple k-NN classifier for classification purpose. Our proposed approaches are tested on Brodatz database. Experimental results on fused features established the combination of two feature sets always outperform the conventional Laws' mask method.