Empirical Mode Decomposition for rotation invariant texture classification

A novel and effective scheme for rotation invariant texture classification is presented using an adaptive and approximately orthogonal filtering process-bidimensional empirical mode decomposition (BEMD). The extraction of rotation invariant feature for a given image involves BEMD and circular zones. A feature vector extracted from circular zones of intrinsic mode function (IMF) is constructed for rotation invariant texture classification. In the experiments, we use rotation invariant feature to classify a set of 25 distinct natural textures selected from the Brodatz album. The experimental results show that the effectiveness of the proposed classification scheme compared with other classification methods.