Texture similarity evaluation using ordinal co-occurrence

Co-occurrence matrices have been successfully used in texture analysis. However, due to noise and monotonic shifts in gray levels, traditional co-occurrence analysis may lead to erroneous results. Using the order of the gray values instead of the gray values themselves is shown to improve the retrieval accuracy. Ordinal measures have been used for many image processing tasks in the literature. In this paper, we propose a novel combination of ordinal measures and co-occurrence matrices using local pixel pair comparisons. Features constructed in this paper represent the occurrence frequency of certain ordinal relationships at different distances and orientations. The proposed method gives encouraging results when comparing its retrieval performance to that of the traditional gray level co-occurrence matrices.