While the paper cited by 'StavrosK is indeed a very important one in computer vision (it's the basis for almost all modern face detection algorithms), it's not

While the paper cited by 'StavrosK is indeed a very important one in computer vision (it's the basis for almost all modern face detection algorithms), it's not the one most relevant for Shazam. I don't know the details of what they're doing, but most likely they are using something related to SIFT: http://en.wikipedia.org/wiki/Scale-invariant_feature_transform http://en.wikipedia.org/wiki/Scale-invariant_feature_transfo... This is another seminal work in computer vision, which solves both problems that 'StavrosK mentioned: 1. Find candidate feature points that stand out, and are reliably and repeatedly detectable despite image variations. 2. Get a "hash" of each point that can be used to do searches fairly quickly. Lots of work in detecting and recognizing objects now uses some variant of SIFT, and it's finding usage in lots of other areas of vision as well. I wouldn't be surprised if as many as 10% of papers at the top vision conferences use techniques based on some variant of SIFT.