Using affine features for an efficient binary feature descriptor

Abstract

A feature descriptor that is robust to a number of image deformations is a basic requirement for vision based applications. Most feature descriptors work well in image deformations such as compression artifacts, illumination changes, and blurring. To develop a feature descriptor that works well apart from these image deformations like transformations caused by long baseline is a challenging task. This paper introduces a compact and efficient binary feature descriptor called PRObabilistic (PRO). A method for removing non-affine features from the initial feature list is developed, which results in further improved performance with the PRO descriptor when dealing with many deformations including long baseline between images. Feature matching accuracy using only affine features is compared with accuracy using both affine and non-affine features on benchmark datasets to demonstrate the advantages of using affine feature point for PRO descriptor.