Features extraction and classification

I'm implementing an ancient coins recognition system. I have used contours detection to extract features of coins. And I thought to use SVM for training images.

My que开发者_Python百科stion is how I can give those features to SVM? I got to know that I have to save those features into a file and then that file should feed to the SVM. But, I don't have an idea to save features to a file.

Saving features to a file means save the number of contours in the image, x, y, width and height of each contour right? Can someone please help me? I am stuck here for two months. Still, I couldn't find the solution for this. Once I save features to a file, do I have to give the coin name also to the same file or to another file?

Appreciate your help a lot.

Nadeeshani

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It depends on what computer vision / image processor library are you using. For example, OpenCV has builtin SVM functionality:

http://opencv.willowgarage.com/documentation/cpp/support_vector_machines.html

so you don't even have to export the features. But LIBSVM ( http://www.csie.ntu.edu.tw/~cjlin/libsvm/) has many more bindings, also for Matlab, for example.

As for how to give the features to the SVM... the input of most categorizers (including SVMs) is a multi-dimensional vector, so you could get one for example concatenating the first 10 x-y-width-height tuples. However, this naive solution is unlikely to work, because if you change the order of the tuples (or you rotate the coin so that the x-y coordinates change), you will get totally different vectors. So try to make up a coin image -> feature vector mapping that doesn't change when the coin is rotated / moved / noise is added. (second idea: features ordered by size, first 5-10, with some shape descriptors instead of simple width / height maybe?)

Coin names are mostly irrelevant at this phase, use 1-of-N encoding for the SVM output.