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copied!<p>Stab in the dark, if you are looking to circumvent meta-data and size related things:</p> <ol> <li>Edge Detection and scale-independent comparison</li> <li>Sampling and statistical analysis of grayscale/RGB values (average lum, averaged color map)</li> <li>FFT and other transforms (Good article <a href="http://cse.cnu.ac.kr/~cheonghee/papers/finger_jrl.pdf" rel="noreferrer">Classification of Fingerprints using FFT</a>)</li> </ol> <p>And numerous others.</p> <p>Basically:</p> <ol> <li>Convert JPG/PNG/GIF whatever into an RGB byte array which is independent of encoding</li> <li>Use a fuzzy pattern classification method to generate a 'hash of the pattern' in the image ... not a hash of the RGB array as some suggest</li> <li>Then you want a distributed method of fast hash comparison based on matching threshold on the encapsulated hash or encoding of the pattern. Erlang would be good for this :)</li> </ol> <p>Advantages are:</p> <ol> <li>Will, if you use any AI/Training, spot duplicates regardless of encoding, size, aspect, hue and lum modification, dynamic range/subsampling differences and in some cases perspective</li> </ol> <p>Disadvantages:</p> <ol> <li>Can be hard to code .. something like <a href="http://opencv.willowgarage.com/" rel="noreferrer">OpenCV</a> might help</li> <li>Probabilistic ... false positives are likely but can be reduced with neural networks and other AI</li> <li>Slow unless you can encapsulate pattern qualities and distribute the search (MapReduce style) </li> </ol> <p>Checkout image analysis books such as:</p> <ol> <li>Pattern Classification 2ed</li> <li>Image Processing Fundamentals</li> <li>Image Processing - Principles and Applications</li> </ol> <p>And others</p> <p>If you are scaling the image, then things are simpler. If not, then you have to contend with the fact that scaling is lossy in more ways than sample reduction.</p>

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