Compact color texture descriptor based on rank transform and product ordering in the RGB color space

Color information is generally considered useful for texture analysis. However, an important category of highly effective texture descriptors – namely rank features – has no obvious extension to color spaces, on which no canonical order is defined. In this work, we explore the use of partial orders in conjunction with rank features. We introduce the rank transform based on product ordering, that generalizes the classic rank transform to RGB space by a combined tally of dominated and non-comparable pixels. Experimental results on nine heterogeneous standard databases confirm that our approach outperforms the standard rank transform and its extension to lexicographic and bit mixing total orders, as well as to the preorders based on the Euclidean distance to a reference color. The low computational com-plexity and compact codebook size of the transform make it suitable for multi-scale approaches.