Rotation-invariant image description from independent component analysis for classification purposes

Independent component analysis (ICA) is a recent technique used in signal processing for feature description in classification systems, as well as in signal separation, with applications ranging from computer vision to economics. In this paper we propose a preprocessing step in order to make ICA algorithm efficient for rotation invariant feature description of images. Tests were carried out on five datasets and the extracted descriptors were used as inputs to the k-nearest neighbor (k-NN) classifier. Results showed an increasing trend on the recognition rate, which approached 100%. Additionally, when low-resolution images acquired from an industrial time-of-flight sensor are used, the recognition rate increased up to 93.33%.