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    <p>A trivial way to compute a hash would be the following. Get all the descriptors from the image (say, N of them). Each descriptor is a vector of 128 numbers (you can convert them to be integers between 0 and 255). So you have a set of N*128 integers. Just write them one after another into a string and use that as a hash value. If you want the hash values to be small, I believe there are ways to compute hash functions of strings, so convert descriptors to string and then use the hash value of that string.</p> <p>That might work if you want to find exact duplicates. But it seems (since you talk about scale, rotation, etc) you want to just find "similar" images. In that case, using a hash is probably not a good way to go. You probably use some interest point detector to find points at which to compute SURF descriptors. Imagine that it will return the same set of points, but in different order. Suddenly your hash value will be very different, even if the images and descriptors are the same.</p> <p>So, if I had to find similar images reliably, I'd use a different approach. For example, I could vector-quantize the SURF descriptors, build histograms of vector-quantized values, and use histogram intersection for matching. Do you really absolutely have to use hash functions (maybe for efficiency), or do you just want to use whatever to find similar images?</p>
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    1. POOpenCV / SURF How to generate a image hash / fingerprint / signature out of the descriptors?
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    1. COHi Sheldon, thank you for your reply. Before looking at SURF I was working with the pHash lib. That lib generates a hash value for media data (images, video and audio). Than you could use the Hamming distance to query your database of similar image hashes quite easily. I thought there must be a way to do the same with the SURF descriptors in some way. Now I'm thinking of storing the all the serialized image descriptor array-objects in the database. When I want to find a similar image in the database I have to select row by row and finding similar descriptors by hand. (...)
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    2. CO(continued) The big downside would be a very bad performance for a large image database. I'm wondering how TinEye.com could find similar images so quickly. There must be some trick to query their database very fast.
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    3. COI think what would help is if you told what exactly are you trying to achieve. Given an image, what are you trying to find? Identical images, similar images, similar images up to rotation/translation scaling? Given an image X, do you want to find only images that are similar to X, or do you also want images that contain X as a sub-part? For example, the query is a face image. Do you want to find other face images, or also full-body photographs that contain the face but also a lot of other stuff? Why did you stop using pHash and decided to work with SURF descriptors instead?
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