Rotation-invariant texture classification using feature distributions

A distribution-based classification approach and a set of recently develope d texture measures are applied to rotation-invariant texture classification . The performance is compared to that obtained with the well-known circular -symmetric autoregressive random field (CSAR) model approach. A difficult c lassification problem of 15 different Brodatz textures and seven rotation a ngles is used in experiments. The results show much better performance for our approach than for the CSAR features. A detailed analysis of the confusi on matrices and the rotation angles of misclassified samples produces sever al interesting observations about the classification problem and the featur es used in this study. (C) 1999 Published by Elsevier Science Ltd. All righ ts reserved.