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).
@article{arxiv.1903.09123,
title = {PProCRC: Probabilistic Collaboration of Image Patches},
author = {Tapabrata Chakraborti and Brendan McCane and Steven Mills and Umapada Pal},
journal= {arXiv preprint arXiv:1903.09123},
year = {2020}
}