Rotationally-blind texture classification using frame sequential approximation error curves

In this paper we report a new set of rotation invariant features for texture classification. The proposed feature set is based on principles of image approximation using multiresolution (MR) frame decompositions. Features are obtained from sequential approximation error curves (SAECs) obtained from the reconstruction error of texture approximations. These approximations are formed by the sequential addition of frame coefficients in decreasing magnitude order. Feature selection consist of taking points along the SAEC. It is found that SAECs are oblivious to rotation, allowing the generation of rotationally blind feature sets. Hence, the computational complexity of classification systems is reduced by eliminating the need for feature post-processing (e.g., DFT-encoding) to achieve rotation invariance (RI). We test the rotationally-blind feature sets for texture classification using different MR frame decompositions and data sets. We show that the proposed SAEC-based feature set achieve classification rates competitive with other schemes using a smaller feature set.