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I am doing a project on histogram based image retrieval, and I need to compare learning algorithms for a set of images. So, in MATLAB, I converted an image (256x256 pixels) into HSV, quantized it to 8(H),3(S),3(V) and created a weighted sum, which is a 256x256 matrix.

I want to use this matrix (of all images in the dataset) to create an ARFF file, and I am stuck at this point. Can anyone help me out with how it has to be done?

vahissan
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If I understood what you did, you took the image as input (256x256 RGB matrix) and converted it to a 256x256 matrix where each position is a weighted sum of HSV values.

However, if you want to extract a color histogram (which, in this case, is the appropriate input to Weka), you should have as output a vector, where each entry is the count of how many pixels has a given H, S and L value.

Since you have 8 different values for H (0 to 7), 3 for S (0 to 2) and 3 for L (0 to 2), your vector V should have 8+3+3=14 entries. In order to compute V, use the following algorithm:

Input: quantized HSL image I
Output: histogram V

for each pixel p in I:
   V[p.H] = V[p.H] + 1             // Increment the count for the H component.
   V[7 + p.S] = V[7 + p.S] + 1     // Increment the count for the S component.
   V[10 + p.L] = V[10 + p.L] + 1   // Increment the count for the L component.

return V
Alceu Costa
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  • Correct me if I am wrong. From what I understand, now I have to forget about the weighted sum, and consider only the matrices for quantized H,S and V values. Then create a vector V with 14 elements each for the values of H(0-7), S(0-2), and V(0-2), then simply count how many pixels have each of these values and convert it to ARFF? – vahissan Feb 08 '12 at 05:09
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    If you want the color histogram that's correct. You can also check if you can get a better classification accuracy by adding more bins to your histogram (i.e. quantize your image with more H,S,V values and create a vector with more than 14 elements). – Alceu Costa Feb 08 '12 at 11:27