  We present a conditional probabilistic framework for collaborative
representation of image patches. It incorporates background compensation and
outlier patch suppression into the main formulation itself, thus doing away
with the need for pre-processing steps to handle the same. A closed form
non-iterative solution of the cost function is derived. The proposed method
(PProCRC) outperforms earlier CRC formulations: patch based (PCRC, GP-CRC) as
well as the state-of-the-art probabilistic (ProCRC and EProCRC) on three
fine-grained species recognition datasets (Oxford Flowers, Oxford-IIIT Pets and
CUB Birds) using two CNN backbones (Vgg-19 and ResNet-50).
