Texture classification by local rank correlation

Abstract

A new approach to texture classification based on local rank correlation is proposed here. Its performance is compared with Laws' method which uses local convolution with feature masks. In the experiments, texture samples are classified based on their distribution of local statistics, either rank correlations or convolutions. The new method achieves generally optimal classification rates. It appears to be more robust because local order statistics are unaffected by local sample differences due to monotonic shifts of texture gray values and are less sensitive to noise.