A novel approach for image feature description based on dual gradient orientation histogram

The robust and distinctive local feature description is a critical component of image matching. In this paper, a novel descriptor, DGOH descriptor, is presented based on dual gradient orientation histogram. DGOH is a full rotation invariant descriptor, which takes advantage of spatial information of features by utilizing intensity order based subregion division method, meanwhile adopts the rotation invariant gradient calculation method in descriptor construction process. The discriminability of DGOH descriptor is enhanced by utilizing dominant and secondary gradient orientation histogram to represent the detected affine feature region. Performance evaluation experiments are carried out on the standard Oxford dataset. The experimental results show that the DGOH descriptor outperforms the state-of-the-art descriptors in terms of stability, precision and efficiency.