title: Extracting Haralick features from GLCM. Why do I get multiple values for each feature?
body: <p>I have seen <a href="https://docs.google.com/viewer?url=https%3A%2F%2Fjp.mathworks.com%2Fmatlabcentral%2Fanswers%2Fuploaded_files%2F25743%2FFinalAlgorithm.pdf" rel="nofollow noreferrer">this</a> paper yesterday. In this paper the features are taken as <strong>contrast</strong>, <strong>local homogeneity</strong> and <strong>energy</strong>, which all are a single values (as per my knowledge) but according to skimage fuction <code>greycomatrix</code>, the parameters passed to these that are <code>distances</code> and <code>angles</code> (which can be more than one).</p>
  
  <p>
  Here is my code:</p>
  
  <pre><code>import numpy as np
  import cv2
  from skimage.feature import greycomatrix, greycoprops
  from skimage import io, color, img_as_ubyte
  
  img = cv2.imread('Grape___Black_rot28.JPG')
  gray_image = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)    
  
  distances = [1, 2, 3]
  angles = [0, np.pi/4, np.pi/2, 3*np.pi/4]
  glcm = greycomatrix(gray_image, 
                      distances=distances, 
                      angles=angles,
                      symmetric=True,
                      normed=True)
  
  properties = ['contrast', 'energy', 'homogeneity', 'correlation', 'dissimilarity']
  contrast = greycoprops(glcm, properties[0])
  energy = greycoprops(glcm, properties[1])
  homogeneity = greycoprops(glcm, properties[2])
  correlation = greycoprops(glcm, properties[3])
  dissimilarity = greycoprops(glcm, properties[4])
  </code></pre>
  
  <p>What confuses me is if I generate a glcm of contrast property it will be of 3x4 size but according to the paper it is a single value and even if I consider all 3x4 values of all the properties as a feature, I bet it will have a over-fitting problem for svm model. </p>