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There are some topics here that are very helpful on how to find similar pictures.

What I want to do is to get a fingerprint of a picture and find the same picture on different photos taken by a digital camera. The SURF algorithm seams to be the best way to be independent on scaling, angle and other distortions.

I'm using OpenCV with the SURF algorithm to extract features on the sample image. Now I'm wondering how to convert all this feature data (position, laplacian, size, orientation, hessian) into a fingerprint or hash.

This fingerprint will be stored in a database and a search query must be able to compare that fingerprint with a fingerprint of a photo with almost the same features.

Update:

It seems that there is no way to convert all the descriptor vectors into a simple hash. So what would be the best way to store the image descriptors into the database for fast querying?

Would Vocabulary Trees be an option?

I would be very thankful for any help.

The feature data you mention (position, laplacian, size, orientation, hessian) is insufficient for your purpose (these are actually the less relevant parts of the descriptor if you want to do matching). The data you want to look at are the "descriptors" (the 4th argument):

void cvExtractSURF(const CvArr* image, const CvArr* mask, CvSeq** keypoints, CvSeq** descriptors, CvMemStorage* storage, CvSURFParams params)

These are 128 or 64 (depending on params) vectors which contain the "fingerprints" of the specific feature (each image will contain a variable amount of such vectors). If you get the latest version of Opencv they have a sample named find_obj.cpp which shows you how it is used for matching

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