A novel local feature descriptor for image matching

Image matching is a fundamental task of many problems in computer vision. This paper presents a novel local feature descriptor based on the gradient distance and orientation histogram (GDOH), which can be used for reliably matching between different views of a scene for wide baseline. The proposed descriptor is invariant to image scale, rotation, illumination and partial viewpoint changes. At present, the SIFT descriptor is generally considered as the most appealing descriptor for practical uses, but the high dimensionality is a drawback of SIFT in the feature matching step. The purpose of GDOH is to reduce the dimensional size of the descriptor, yet still maintain distinctness and robustness as much as SIFT. The experimental results show that the proposed descriptor can result in effectiveness and efficiency in image matching and image retrieval application.