A Feature Descriptor by Difference of Polynomials

In this paper, we propose a novel local image descriptor DoP which is termed as the difference of images represented by polynomials in different degrees. Once an interest point/region is extracted by a common image detector such as Harris corner, our DoP descriptor is able to characterize the interest point/region with high distinctiveness, compactness, and robustness to viewpoint change, image blur, and illumination variation. To efficiently build DoP descriptor, we propose to numerically reduce the computational cost by jumping over the repeatedly calculating polynomial representation. Our experimental results demonstrate a better performance compared to several state-of-art candidates.