Mining Discriminative Triplets of Patches for Fine-Grained Classification

Yaming Wang, Jonghyun Choi, Vlad Morariu, Larry S. Davis; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 1163-1172

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 obtains comparable results to the state-of-the-art in classification.

Related Material


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[bibtex]
@InProceedings{Wang_2016_CVPR,
author = {Wang, Yaming and Choi, Jonghyun and Morariu, Vlad and Davis, Larry S.},
title = {Mining Discriminative Triplets of Patches for Fine-Grained Classification},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2016}
}