C++ - Using Bag of Words for matching pictures together?

Question

I would like to compare a picture (with his descriptors) with thousand of pictures inside a database in order to do a matching. (if two pictures are the same,that is to say the same thing but it can bo rotated, a bit blured, has a different scale etc.).

For example :

I saw on StackOverflaw that compute descriptors for each picture and compare them one to one is very a long process. I did some researches and i saw that i can do an algorithm based on Bag of Words.

I don't know exactly how is works yet, but it seems to be good. But in think, i can be mistaked, it is only to detect what kind of object is it not ?

I would like to know according to you if using it can be a good solution to compare a picture to a thousands of pictures using descriptors like Sift of Surf ?

If yes, do you have some suggestions about how i can do that ?

Thank,

Answer 1

Yes, it is possible. The only thing you have to pay attention is the computational requirement which can be a little overwhelming. If you can narrow the search, that usually help.

To support my answer I will extract some examples from a recent work of ours. We aimed at recognizing a painting on a museum's wall using SIFT + RANSAC matching. We have a database of all the paintings in the museum and a SIFT descriptor for each one of them. We aim at recognizing the paining in a video which can be recorded from a different perspective (all the templates are frontal) or under different lighting conditions. This image should give you an idea: on the left you can see the template and the current frame. The second image is the SIFT matching and the third shows the results after RANSAC.

Once you have the matching between your image and each SIFT descriptor in your database, you can compute the reprojection error, namely the ratio between matched points (after RANSAC) and the total number of keypoints. This can be repeated for each image and the image with the lowest reprojection error can be declared as the match.

We used this for paintings but I think that can be generalized for every kind of image (the android logo you posted in the question is a fair example i think).

Hope this helps!