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arXiv · 1605.01130

Mining Discriminative Triplets of Patches for Fine-Grained Classification

Yaming Wang·Jonghyun Choi·Vlad I. Morariu·Larry S. Davis
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Abstract

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.

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Yaming Wang, Jonghyun Choi, Vlad I. Morariu, Larry S. Davis. 2016-05-04. Mining Discriminative Triplets of Patches for Fine-Grained Classification. https://arxiv.org/abs/1605.01130

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