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2016

Fused One-vs-All Features With Semantic Alignments for Fine-Grained Visual Categorization

3 years 6 months ago
Fused One-vs-All Features With Semantic Alignments for Fine-Grained Visual Categorization
—Fine-grained visual categorization is an emerging research area and has been attracting growing attention recently. Due to the large inter-class similarity and intra-class variance, it is extremely challenging to recognize objects in fine-grained domains. A traditional spatial pyramid matching model could obtain desirable results for the basic-level category classification by weak alignment, but may easily fail in fine-grained domains, since the discriminative features are extremely localized. This paper proposes a new framework for fine-grained visual categorization. First, an efficient part localization method incorporates semantic prior into geometric alignment. It detects the less deformable parts, such as the head of birds with a template-based model, and localizes other highly deformable parts with simple geometric alignment. Second, we learn one-vs-all features, which are simple and transplantable. The learned mid-level features are dimension friendly and more robust to ...
Xiaopeng Zhang 0001, Hongkai Xiong, Wengang Zhou,
Added 11 Apr 2016
Updated 11 Apr 2016
Type Journal
Year 2016
Where TIP
Authors Xiaopeng Zhang 0001, Hongkai Xiong, Wengang Zhou, Qi Tian
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