CODEWITHSUNDEEP
matlabcomputer-visionsignal-processingfftdata-mining
Bob Fang
Bob Fang

Reputation: 7381

How to extract useful features from a graph?

Things are like this:

S

T

I have some graphs like the pictures above and I am trying to classify them to different kinds so the shape of a character can be recognized, and here is what I've done:

I apply a 2-D FFT to the graphs, so I can get the spectral analysis of these graphs. And here are some result:

resultS

S after 2-D FFT

resultT

T after 2-D FFT

I have found that the same letter share the same pattern of magnitude graph after FFT, and I want to use this feature to cluster these letters. But there is a problem: I want the features of interested can be presented in a 2-D plane, i.e in the form of (x,y), but the features here is actually a graph, with about 600*400 element, and I know the only thing I am interested is the shape of the graph(S is a dot in the middle, and T is like a cross). So what can I do to reduce the dimension of the magnitude graph?

I am not sure I am clear about my question here, but thanks in advance.

Upvotes: 1

Views: 755

Answers (1)

Sanjay Manohar
Sanjay Manohar

Reputation: 7026

You can use dimensionality reduction methods such as

  • k-means clustering
  • SVM
  • PCA
  • MDS

Each of these methods can take 2-dimensional arrays, and work out the best coordinate frame to distinguish / represent etc your letters. One way to start would be reducing your 240000 dimensional space to a 26-dimensional space using any of these methods. This would give you an 'amplitude' for each of the possible letters.

But as @jucestain says, a network classifiers are great for letter recognition.

Upvotes: 1

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