similarity between an image and its rotated version using SIFT

I have implemented SIFT in opencv for comparing images... i have not yet written the program for comparing.Thinking of using FLANN for the same.But,my problem is that,looking into the 128 elements of the descriptor,cannot really understand the similarity of an image and its rotated version.

By reading Lowe's paper,i do un开发者_StackOverflowderstand that the descriptor co-ordinates are all rotated in terms of the keypoint orientation...but,how exactly is the similarity obtained.Can we undertstand the similarity by just viewing the 128 values.

pls,help me...this is for my project presentation.

You can first use Lowe's metric to compute some putative matches between the two images. The metric is that for any given descriptor de in image 1, find the distance to all descriptors de' in image 2. If the ratio of the closest distance to the second closest distance is below a threshold, then accept it.

After this, you can do RANSAC or other form of robust estimation or Hough Transform to check geometric consistency in terms of position, orientation, and scale of the keypoints that you accepted as putative matches.

If I recall correctly, SIFT will give you a set of 128-value descriptors that describe each of the interest points. You also have the location of each point in each of the images, as well as its "direction" (I forget what the "direction" is called in the paper) and scale in each image.

Once you've found two points that have matching descriptors, you can calculate the transformation from the interest point in one image to the same point in the other image by comparing coordinates and directions.

If you have enough matches, you see if all (or a majority of) the interest points have the same transformation. If they do, the images are similar, if they don't, the images are different.

Hope this helps...

What you are looking for is basically ASIFT

You can find the code here and some overview