Color feature detection and classification by learning

In this paper, we aim at the classification of the physical nature of local image structures in color images on the basis of geometrical and photometrical information. To this end, a framework is proposed to combine the local differential structure (i.e. geometrical information such as edges, corners, T-junctions etc) and color (i.e. photometrical information such as shadows, shading, illumination, highlights) in a multi-dimensional feature space. This framework is used to yield a proper classifier to classify salient image structures on the basis of their physical nature. The proposed framework is empirically verified on a set of images. From the theoretical and experimental results it is concluded that the proposed classification scheme successfully classifies local image structures robust to image translation and rotation, illumination intensity variations, and noise.