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arXiv · 1903.09123

PProCRC: Probabilistic Collaboration of Image Patches

Tapabrata Chakraborti·Brendan McCane·Steven Mills·Umapada Pal
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Abstract

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).

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Tapabrata Chakraborti, Brendan McCane, Steven Mills, Umapada Pal. 2019-03-21. PProCRC: Probabilistic Collaboration of Image Patches. https://arxiv.org/abs/1903.09123

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