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.
