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    <p>I think speed is not the main issue here. The main issue is how to use the features to get the results you want. </p> <p>If you want to categorize the images (e. g. person, car, house, cat), then the Pyramid Match kernel is definitely worth looking at. It is actually a histogram of the local feature descriptors, so there is no need to compare individual features to each other. There is also a class of algorithms known as the "bag of words", which try to cluster the local features to form a "visual vocabulary". Again, in this case once you have your "visual words" you do not need to compute distances between all pairs of SIFT descriptors, but instead determine which cluster each feature belongs to. On the other hand, if you want to get point correspondences between pairs of images, such as to decide whether one image is contained in another, or to compute the transformation between the images, then you do need to find the exact nearest neighbors.</p> <p>Also, there are local features other than SIFT. For example SURF are features similar to SIFT, but they are faster to extract, and they have been shown to perform better for certain tasks.</p> <p>If all you want to do is to find duplicates, you can speed up your search considerably by using a global image descriptor, such as a color histogram, to prune out images that are obviously different. Comparing two color histograms is orders of magnitude faster than comparing two sets each containing hundreds of SIFT features. You can create a short list of candidates using color histograms, and then refine your search using SIFT.</p>
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    1. POComparing SIFT features stored in a mysql database
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    1. COThe Pyramid Kernel seems interesting indeed, since it aggregates the data more so there is less to search over. My main goal is however not object recognition or categorizing, but finding duplicates that have been significantly transformed, so a simple histogram matching is unfortunately insufficient.
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    2. COOf course, in your case, histogram matching is insufficient. But it will allow you to quickly reject a large percentage of non-duplicates. Think of it as a two-stage process: if the color histograms match, only then try match SIFT features.
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