Mining Discriminative Triplets of Patches for Fine-Grained Classification


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.

DOI: 10.1109/CVPR.2016.131

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@article{Wang2016MiningDT, 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={2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year={2016}, pages={1163-1172} }