A new framework for feature descriptor based on SIFT

The description of interest points is a critical aspect of point correspondence which is vital in some computer vision and pattern recognition tasks. SIFT descriptor has been proven to perform better on the distinctiveness and robustness than other local descriptors. But SIFT descriptor does not involve color and global information of feature point which provides powerfully distinguishable signals in feature description and matching tasks, so many mismatches may occur. This paper improves SIFT descriptor, and presents a new framework for feature descriptor based on SIFT by integrating color and global information with it. The proposed framework consists of the improved SIFT, color invariance components and global component. We use a log-polar histogram to build three color invariance components and the global component of the proposed framework, respectively. In addition, the elliptical neighboring region for every interest point is used so as to make the framework fully invariant to common affine transformations. Experimental comparison with three related feature descriptors is carried out in two groups of experiments, validating the proposed framework.