Fast Representation Based on a Double Orientation Histogram for Local Image Descriptors

In recent years, extensive research on local invariant features has been conducted, and many novel descriptors have been developed for different scenarios. Frequently, these descriptors can offer unique advantages (such as stability, precision, and speed) for select applications but perform unsatisfactorily from a comprehensive perspective. Consequently, a novel local image descriptor, named fast representation using a double orientation histogram (FRDOH), is developed in this paper based on existing descriptors. First, a region is divided using intensity order (as in the local intensity order pattern descriptor) to encode spatial information. Then, the discriminability of the descriptor is enhanced using our proposed double orientation histogram. Second, to further improve the discriminability of the descriptor, the Hellinger distance is used to balance the effects of large and small bins in the histogram for the similarity measure of features. Finally, a novel interpolation strategy known as rapidly cascaded interpolation is used to calculate the intensity of the neighboring points to reduce the computation time, while achieving high precision. The performance of the developed descriptor is evaluated via numerous experiments on the affine covariant feature data set of the Oxford data set, a subset of a 3D object data set, and a subset of the IIT Delhi Touchless Palmprint data set. These experiments demonstrate that the developed FRDOH descriptor outperforms the state-of-the-art descriptors in terms of comprehensive performance.