1. A method, comprising: identifying a plurality of data sets, each data set of the plurality of data sets associated with a distribution model and each data set of the plurality of data sets associated with an image having a first noise level; partitioning the data sets into a plurality of groups, the partitioning including clustering the data sets the clustering comprising: splitting the data into two subsets using k-means clustering; testing each of the two subsets for normality; and recursively splitting each non-normal subset into an additional two subsets until either each of the additional two subsets pass the normality test or has fewer members than a predetermined threshold; annotating each group of the plurality of groups; receiving an input data set; determining which group is a closest match to each input data set; and annotating the input data set according to the group having the closest match, wherein at least one method operation is executed through a processor.