Grey Level Co-ocurrence Matrix Properties

[scottclowe]

Properties extracted from the GLCM are useful for measuring texture.

Step 1: Compute GLCM from image with greycomatrix for a generous number of distances, for all 4 angles. http://scikit-image.org/docs/dev/api/skimage.feature.html#greycomatrix

We might want to drop the 255-to-255 histogram, since that is the background colour? And then normalise against number of remaining pixels, because the images are different sizes?

Step 2: Compute all 6 properties which greycoprops can compute, for each distance and angle. http://scikit-image.org/docs/dev/api/skimage.feature.html#greycoprops

Step 3: Average over angles.

Step 4: Return a feature vector sized 6 * number_of_distances

Clearly, number_of_distances is <= the minimum length of a side of an image in either test or train. This number is about 20, so the response vector contains about 120 elements.

[scottclowe]

NB: these features are similar to the Haralick features (see #73), but not quite the same. The skimage implementation can do multiple distances, which mahotas cannot.

[scottclowe]

This should subclass HighLevelFeatures.