Fine-grained classification involves distinguishing between similar sub-categories based on subtle differences in highly localized regions; therefore, accurate localization of discriminative regions remains a major challenge. We describe a patch-based framework to address this problem. We introduce triplets of patches with geometric constraints to improve the accuracy of patch localization, and automatically mine discriminative geometrically-constrained triplets for classification. The resulting approach only requires object bounding boxes. Its effectiveness is demonstrated using four publicly available fine-grained datasets, on which it outperforms or achieves comparable performance to the state-of-the-art in classification.
@article{arxiv.1605.01130,
title = {Mining Discriminative Triplets of Patches for Fine-Grained Classification},
author = {Yaming Wang and Jonghyun Choi and Vlad I. Morariu and Larry S. Davis},
journal= {arXiv preprint arXiv:1605.01130},
year = {2016}
}