Efficient texture regularity estimation for second order statistical descriptors

Co-occurrence matrices as sources of second order statistical descriptors are commonly used in texture classification tasks. To generate such a matrix, we need a position vector to check possible intensity frequencies in its endpoints. In this paper, we propose an efficient algorithm to locate such position vectors according which the pattern of the texture repeats and thus, the descriptors (Haralick features) derived from the co-occurrence matrix are capable to characterize the regularity of the pattern. The essence of our approach is to look for vectors that span well-approximating grids defined by reference points obtained by quantizing the input image. To extract such grids we use the LLL algorithm, which has a polynomial running time. Thus, we have a much more efficient solution than e.g. a brute force based search. Our results show that the proposed approach is capable to suggest position vectors for an efficient co-occurrence matrix based texture analysis.