Joint Semantic Parts for Fine-Grained Bird Images Recognition

Fine-grained image recognition is a challenging computer vision problem, due to small inter-classvariations caused by highly similar subordinate categories, and the large intra-class variations in poses,scales and rotations. In order to perform fine-grained recognition on bird images, this paper proposes a deepconvolution neural networks model collaborated with semantic parts detection. The model consists of twomodules, one module is a parts detector network, and another module is a three-stream classification networkbased on deep residual network. In the meantime, a new bird images dataset was collected and labeled to facilitythe research of fine-grained bird images recognition. Experiment results on YUB-200-2017 andCUB-200-2011 illustrate the proposed model has higher part detection and image classification accuracycomparing with state-of-the-arts fine-grained bird image recognition approaches.