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    <p>You could try to use local features like SIFT here: <a href="http://en.wikipedia.org/wiki/Scale-invariant_feature_transform" rel="noreferrer">http://en.wikipedia.org/wiki/Scale-invariant_feature_transform</a></p> <p>It should work because logo shape is usually constant, so extracted features shall match well.</p> <p>The workflow will be like this:</p> <ol> <li><p>Detect corners (e.g. Harris corner detector) - for Nike logo they are two sharp ends.</p></li> <li><p>Compute descriptors (like SIFT - 128D integer vector)</p></li> <li><p>On training stage remember them; on matching stage find nearest neighbours for every feature in the database obtained during training. Finally, you have a set of matches (some of them are probably wrong).</p></li> <li><p>Seed out wrong matches using RANSAC. Thus you'll get the matrix that describes transform from ideal logo image to one where you find the logo. Depending on the settings, you could allow different kinds of transforms (just translation; translation and rotation; affine transform).</p></li> </ol> <p>Szeliski's book has a chapter (4.1) on local features. <a href="http://research.microsoft.com/en-us/um/people/szeliski/Book/" rel="noreferrer">http://research.microsoft.com/en-us/um/people/szeliski/Book/</a></p> <p>P.S. </p> <ol> <li><p>I assumed you wanna find logos in photos, for example find all Pepsi billboards, so they could be distorted. If you need to find a TV channel logo on the screen (so that it is not rotated and scaled), you could do it easier (pattern matching or something).</p></li> <li><p>Conventional SIFT does not consider color information. Since logos usually have constant colors (though the exact color depends on lightning and camera) you might want to consider color information somehow.</p></li> </ol>
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    1. POLogo recognition in images
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    1. USRoman Shapovalov
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    1. COThanks. This approach sounds reasonable. Regarding the nearest neighbor for every feature - that sounds pretty intensive (I'm planning on having thousands of logos to be recognized), what would you think is a good way of optimizing? I thought of vector quantization or approximate nearest neighbors...
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    2. COliza, you are right, it is hard to find the NN in 128D. The current state-of-the-art is approximate NN search via kd-tree or k-means tree forest. It is implemented in Muja-Lowe FLANN: http://people.cs.ubc.ca/~mariusm/index.php/FLANN/FLANN
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      1. USRoman Shapovalov
    3. COThanks again. Also found these papers dealing with scalable and efficient image recognition: * "Small Codes and Large Image Databases for Recognition" by Torralba,Fergus,Weiss * "Scalable Recognition with a Vocabulary Tree" by Nister and Stewenius
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