Scale invariant feature transform evaluation in small dataset

This paper investigates how we can achieve object recognition in an image by looking at some examples of training images. Scale Invariant Feature Transform (SIFT) is one proposal method to detect features in an image and then can use those features to distinguish between different objects. Therefore, my aim was to implement SIFT code to do recognition tasks using simple thresholding and evaluating this algorithm to find its strength and weakness points for a small dataset. The challenge here is to find the best threshold for examples of training images, which can work properly with query images.