IBC127: Video Dataset for Fine-grained Bird Classification
Beyond general object recognition whereby general categories such as dogs and cats are estimated from images, Finegrained visual categorization (FGVC) is a new trend that goes beyond general object recognition―where general categories such as cats and dogs are estimated from images―to classify fine-grained categories of objects (or animals) such as poodles or bulldogs. It is difficult to distinguish between categories with similar appearance, e.g., sparrows and hummingbirds, using image features alone. Consequently, motion features extracted from videos are effective for classifying similar animals. In this paper, we demonstrate the effectiveness of motion features for FGVC of animals. We use our novel dataset that consists of videos of fine-grained categories of birds collected from the internet. Our dataset is publicly available for academics.
Tomoaki Saito, Asako Kanezaki, and Tatsuya Harada, "IBC127: Video Dataset for Fine-grained Bird Classification", IEEE International Conference on Multimedia & Expo (ICME), Seattle, America, 2016.
[IBC127 Dataset (link to Google Drive)]
Contact: saito (at) mi.t.u-tokyo.ac.jp, harada (at) mi.t.u-tokyo.ac.jp