Images of many surfaces can be considered as stochastic textures, hence
the co-occurrence matrices [6], also known as the spatial gray
level dependence matrices, are used for the texture description.
The co-occurrence matrix method has been found to be good in classifying
stochastic textures [16, 3]. The co-occurrence matrix
is a second order statistical measure of gray level variation.
It indicates the joint probability of gray level occurrence at a certain
displacement in an image.
The co-occurrence matrix can be reduced to make calculation time and memory requirements smaller. One approach is to concatenate the rows and columns into two vectors. Another approach is to calculate a set of features [6].
The co-occurrence matrices are calculated locally within a small window that glides across the image. The window size is also a compromise. The size of the window should be small that the lowpass filtering effect due to masking remains tolerable, but it should also be large enough so that the extracted information has statistical significance.