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 ...

• Type: Journal
• Year: 2016
• Where: TIP
• Authors: Xiaopeng Zhang 0001, Hongkai Xiong, Wengang Zhou, Qi Tian
• Keywords: Education, TIP 2016