The impact of information volume on SIFT descriptor

Abstract

This paper provides a performance evaluation on the Scale- invariant Feature Transform (SIFT) descriptors that utilise different sizes of image patches to represent the SIFT keypoints in images. Although SIFT has been widely employed in numerous applications such as object recognition and image registration, its performances against different image complexities and transformations are still unclear. Thus, an evaluation is commenced to examine SIFT descriptor's performance while its dimension (i.e., information volume) is varied. This paper is started by providing the general concept of SIFT descriptor, then the experimental setup and evaluation metrics are described for detailing the performance evaluation. The experimental results are shown by two evaluation metrics that are repeatability and recall-precision. Lastly, discussions and conclusions are included to emphasise the significances observed in the experimental results and highlight possible directions for future work.