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...

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Bibliographic Details
Published in2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) pp. 1163 - 1172
Main Authors Yaming Wang, Jonghyun Choi, Morariu, Vlad I., Davis, Larr S.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2016
Subjects
Data mining
Detectors
Feature extraction
Noise measurement
Shape
Training
Visualization
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Summary: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.
ISSN:1063-6919
DOI:10.1109/CVPR.2016.131