Evaluating the quality of individual SIFT features

Scale-Invariant Feature Transform (SIFT) is one of the most popular local image features that are widely used in computer vision, image processing and image retrieval. In this paper we study the relation between the SIFT descriptor and its matching accuracy. We propose a method to quantitatively assess the quality of a SIFT feature descriptor in terms of robustness and discriminability. This would enable us to gain a better understanding of the strength and limitations of SIFT in emerging applications of SIFT-based image hash, and also to improve matching accuracy and efficiency in applications such as object search. The experimental results demonstrate the effectiveness of the proposed method.