One of the most successful features for texture recognition is the Local Binary Pattern. The LBP is the 8 digit binary number created by comparing the value of a central pixel with its 8 neighbours where 1s and 0s are assigned when respectively the central pixel is larger or smaller
than its neighbour. This pattern is bit shifted circularly to its maximum value to obtain rotational invariance. Comparing histograms of LBPs provides leading texture recognition.
In our research, we rank the center pixel with all its 8 neighbours. Each pixel is substituted by a
3x3 grid where the numbers one through nine appear once and correspond to the rank of the underlying pixel values (of the local 3x3 neighbourhood) i.e. an input image is transformed to look like a Sudoku grid. Then, we read out the ranks clockwise starting with the right-most rank and appending
the central pixel to the end, we then rotate to the maximum value (so achieving rotational invariance). Each 9 digit number is non-linearly mapped to the interval [0,1] so that the overall dataset histogram has a uniform distribution. By comparing the histograms of our Sudoku rank features,
we observe a significant increase in recognition performance for the Outex and Curet benchmark datasets.