  Pruning of redundant or irrelevant instances of data is a key to every
successful solution for pattern recognition. In this paper, we present a novel
ranking-selection framework for low-length but highly correlated instances.
Instead of working in the low-dimensional instance space, we learn a supervised
projection to high-dimensional space spanned by the number of classes in the
dataset under study. Imposing higher distinctions via exposing the notion of
labels to the instances, lets to deploy one versus all ranking for each
individual classes and selecting quality instances via adaptive thresholding of
the overall scores. To prove the efficiency of our paradigm, we employ it for
the purpose of texture understanding which is a hard recognition challenge due
to high similarity of texture pixels and low dimensionality of their color
features. Our experiments show considerable improvements in recognition
performance over other local descriptors on several publicly available
datasets.
