I was wondering if it exists any other technique as the one which consist of pairwise check on 2 images descriptors proximity to find, in a really huge set of images, which are the pairs which share a portion of the same area or objects?
Because finding matches need to compute every possible combinations, 2 by 2, which dramatically increase the computation time when datasets are really big.
I'd like to find a faster way (at this point, I don't really need to check for precise matching, I just have to be sure that 2 images may share some same information to make a fast clustering).
Any idea (even outside openCV) ? Thanks.