Improving the SIFT descriptor with smooth derivative filters

Several approaches to object recognition make extensive use of local image information extracted in interest points, known as local image descriptors. State-of-the-art methods perform a statistical analysis of the gradient information around the interest point, which often relies on the computation of image derivatives with pixel differencing methods. In this paper, we show the advantages of using smooth derivative filters instead of pixel differences in the performance of a well known local image descriptor. The method is based on the use of odd Gabor functions, whose parameters are selectively tuned to as a function of the local image properties under analysis. We perform an extensive experimental evaluation to show that our method increases the distinctiveness of local image descriptors for image region matching and object recognition.