Weakly Supervised Fine-Grained Categorization With Part-Based Image Representation

—In this paper, we propose a fine-grained image categorization system with easy deployment. We do not use any object/part annotation (weakly supervised) in the training or in the testing stage, but only class labels for training images. Finegrained image categorization aims to classify objects with only subtle distinctions (e.g., two breeds of dogs that look alike). Most existing works heavily rely on object/part detectors to build the correspondence between object parts, which require accurate object or object part annotations at least for training images. The need for expensive object annotations prevents the wide usage of these methods. Instead, we propose to generate multiscale part proposals from object proposals, select useful part proposals, and use them to compute a global image representation for categorization. This is specially designed for the weakly supervised fine-grained categorization task, because useful parts have been shown to play a critical role in existing ann...

• Type: Journal
• Year: 2016
• Where: TIP
• Authors: Yu Zhang 0004, Xiu-Shen Wei, Jianxin Wu, Jianfei Cai, Jiangbo Lu, Viet Anh Nguyen, Minh N. Do
• Keywords: Education, TIP 2016