Fractional Differentiation-based Image Feature Extraction

Two novel methods for image feature extraction based on fractional differentiation are presented in this paper. The first method is the feature extraction of fusing multi-direction CRONE operators. In this method, the fractional differential CRONE mask is generalized to eight directions at first for extracting image features; then the extracted features are tested by the statistic method and fused by the gradient ratio, so that the outlines of the objects in the image are obtained. In order to extract the detail feature information in the image effectively, the second method, the ‘S+Z’ extraction combined with the space-filling curves, is presented. By introducing the space-filling curves, the ‘S’ curve and the ‘Z’ curve, and making full use of the neighborhood information of image pixels, the detailed features of the objects in the image are obtained. The experiment results show that our methods can obtain satisfactory image features.