Ranklet
Introduction In statistics, a '''ranklet''' is an orientation-selective non-parametric feature which is based on the computation of Mann–Whitney–Wilcoxon (MWW) rank-sum test statistics. Ranklets achieve similar response to Haar wavelets as they share the same pattern of orientation-selectivity, multi-scale nature and a suitable notion of completeness. There were invented by Fabrizio Smeralhi in 2002.Rank-based (non-parametric) features have become popular in the field of image processing for their robustness in detecting outliers and invariance to monotonic transformations such as brightness, contrast changes and gamma correction.The MWW is a combination of Wilcoxon rank-sum test and Mann–Whitney U-test. It is a non-parametric alternative to the t-test used to test the hypothesis for the comparison of two independent distributions. It assesses whether two samples of observations, usually referred as Treatment '''''T''''' and Control '''''C''''', come from the same distribution but do not have to be normally distributed.The Wilcoxon rank-sum statistics ''W''''s'' is determined as:: Subsequently, let ''MW'' be the Mann–Whitney statistics defined by:: where ''m'' is the number of Treatment values.A ranklet ''R'' is defined as the normalization of ''MW'' in the range −1, +1:: where a positive value means that the Treatment region is brighter than the Control region, and a negative value otherwise.
Example Suppose  and  then '''Intensity''' 1 4 5 7 9 10 11 13 15 19 20 '''Sample''' T C T C T  T C C T C C '''Rank''' 1 2 3 4 5 6 7 8 9 10 11***Hence, in the above example the Control region was a little bit brighter than the Treatment region.
Method Since Ranklets are non-linear filters, they can only be applied in the spatial domain. Filtering with Ranklets involves dividing an image window ''W'' into Treatment and Control regions as shown in the image below:Orientation-Selective RankletsSubsequently, Wilcoxon rank-sum test statistics are computed in order to determine the intensity variations among convenientlychosen regions (according to the required orientation) of the samples in ''W''. The intensity values of both regions are thenreplaced by the respective ranking scores. These ranking scores determine a pairwise comparison between the '''''T''''' and '''''C''''' regions. This means that a ranklet essentially counts the number of '''T'''x'''C''' pairs which are brighter in the '''''T''''' set. Hence a positive value means that the Treatment values are brighter than the Control values, and vice versa.
References
External links * Matlab  RankletFilter.m -> source file to decompose an image into Intensity Ranklets