

Ask HN: Completely crazy idea for an image classification system? - jacquesm

Image classification is a tough problem, and as far as I know nobody has ever really solved it in a way that you could rely on it.<p>So, if that's the case why not brute force it ?<p>Imagine a fingerprint of 40x30 pixels, 3/3/2 (r/g/b) for a given image, with an associated set of tags.<p>There are 2^9600 (40x30x8) possible prints like that, which is obviously an astronomical number, well over the number of atoms in the universe.<p>But because you can compute the distance from one print to another in a relatively simple way (the sum of the squares of the distances of the r, g and b components of every pixel) you could find the closest 'match' by comparing against a library of previously tagged content.<p>Is there anybody here that can take a stab at how big such a library should be to get a given level of confidence ?<p>My gut tells me that it is probably still a very large number but knowing roughly how large that number is would help a lot in judging if this is madness, hard or doable.<p>thanks!
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pgbovine
you might want to take a look at the Shazam song classification system, which
essentially does a 'brute force' search like the one you sketched out, except
with a lot of cleverness to make it run super-fast

research paper: <http://www.ee.columbia.edu/~dpwe/papers/Wang03-shazam.pdf>

blog entry summarizing paper: <http://laplacian.wordpress.com/2009/01/10/how-
shazam-works/>

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frankus
Check out the [SIFT Algorithm]([http://en.wikipedia.org/wiki/Scale-
invariant_feature_transfo...](http://en.wikipedia.org/wiki/Scale-
invariant_feature_transform)). It's not exactly what you describe but might
give you some ideas.

