Large-Scale Fine-Grained Bird Recognition Based on a Triplet Network and Bilinear Model

• Main Authors: Zhicheng Zhao, Ze Luo, Jian Li, Kaihua Wang, Bingying Shi
• Format: Article
• Language: English
• Published: MDPI AG 2018-10-01
• Series: Applied Sciences
• Subjects: bird recognition; fine-grained; triplet network; bilinear model; Xception
• Online Access: http://www.mdpi.com/2076-3417/8/10/1906

The main purpose of fine-grained classification is to distinguish among many subcategories of a single basic category, such as birds or flowers. We propose a model based on a triple network and bilinear methods for fine-grained bird identification. Our proposed model can be trained in an end-to-end manner, which effectively increases the inter-class distance of the network extraction features and improves the accuracy of bird recognition. When experimentally tested on 1096 birds in a custom-built dataset and on Caltech-UCSD (a public bird dataset), the model achieved an accuracy of 88.91% and 85.58%, respectively. The experimental results confirm the high generalization ability of our model in fine-grained image classification. Moreover, our model requires no additional manual annotation information such as object-labeling frames and part-labeling points, which guarantees good versatility and robustness in fine-grained bird recognition.