Learning How to Extract Rotation-Invariant and Scale-Invariant Features from Texture Images. (28th May 2008) - Montoya-Zegarra Montoya-Zegarra, Javier A. Javier A.; Papa Papa, João Paulo João Paulo; Leite Leite, Neucimar J. Neucimar J.; da Silva Torres da Silva Torres, Ricardo Ricardo; Falcão Falcão, Alexandre X. Alexandre X.
Abstract : Learning how to extract texture features from noncontrolled environments characterized by distorted images is a still-open task. By using a new rotation-invariant and scale-invariant image descriptor based on steerable pyramid decomposition, and a novel multiclass recognition method based on optimum-path forest, a new texture recognition system is proposed. By combining the discriminating power of our image descriptor and classifier, our system uses small-size feature vectors to characterize texture images without compromising overall classification rates. State-of-the-art recognition results are further presented on the Brodatz data set. High classification rates demonstrate the superiority of the proposed system.