2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)
Las Vegas, NV, United States
June 27, 2016 to June 30, 2016
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CVPR.2016.131
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.
Feature extraction, Training, Shape, Detectors, Visualization, Noise measurement, Data mining
Y. Wang, J. Choi, V. I. Morariu and L. S. Davis, "Mining Discriminative Triplets of Patches for Fine-Grained Classification," 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, United States, 2016, pp. 1163-1172.