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copied!<p>I think that google images uses a 3 combined algorithm </p> <ol> <li>detect image's deformation (by rotation, scale, illumination,...) </li> <li>detect by the colors </li> <li>detect by the visual similarity</li> </ol> <p>and a fourth algorithm that is a secret by google (to ranking for example) ;)</p> <p>(see here -> <a href="http://www.quora.com/Algorithms/What-is-the-algorithm-used-by-Google-Search-by-Image-1" rel="nofollow">http://www.quora.com/Algorithms/What-is-the-algorithm-used-by-Google-Search-by-Image-1</a> )</p> <hr> <p><strong>Update 2016</strong></p> <p>My original answer was on 2012 - in the meanwhile other studies and research have taken more and more importance and I learn some new stuff. ;-)</p> <p>In my opinion the mains "philosophies" about image detections are three:</p> <ol> <li><a href="https://en.wikipedia.org/wiki/Machine_learning" rel="nofollow">machine learning</a> algorithms</li> <li><a href="https://en.wikipedia.org/wiki/Deep_learning" rel="nofollow">deep learning</a> (a machine learning algorithm)</li> <li>(<a href="https://en.wikipedia.org/wiki/Pattern_recognition" rel="nofollow">pattern recognition</a>)</li> </ol> <p>Today, I think that pattern recognition has lost its importance: machine learning is in my opinion the right way to work for searching by image. </p> <p>With machine learning you can even search for similarly match (for example faces - that obviously are not equals between them). The difficult is how you will to teach correctly your machine. Different approach can be taken.</p> <p>Deep learning is simply a machine learning algorithm. It goes deeper using differents layers to match a possible image, some example of layers could be:</p> <ul> <li>pixel-vector (color, intensity,..)</li> <li>shapes</li> <li>edges</li> <li>...</li> </ul>

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