COGE: A novel binary feature descriptor exploring anisotropy and non-uniformity

Abstract

Matching keypoints across images is the base of numerous Computer Vision applications, which is often done with local feature descriptors. Handcrafted descriptors such as SIFT and SURF are still established leaders in the field since they are discriminative as well as robust. In this paper, we introduce a novel COGE descriptor, a simple yet effective method for keypoint description. By exploiting the anisotropy and the nonuniformity of the underlying gradient distributions, the proposed COGE is highly discriminative and robust. In addition, COGE contains only 480/240/120 bits and can be matched by using Hamming distance, making it ideal for mobile applications. To evaluate the performance of COGE, a comprehensive comparison against SIFT, SURF, ORB and BRISK is performed on three benchmark datasets: The dataset of Mikolajczyk, the INRIA Holidays and the UKbench. Experimental results show that our proposed COGE descriptor significantly outperforms existing schemes. © Springer International Publishing Switzerland 2013.