# Large-Scale Fine-Grained Bird Recognition Based on a Triplet Network and Bilinear Model
## 5 Discussion  
In this paper, we propose a model based on a triplet network and bilinear methods for fine-grained bird recognition. Our proposed model was easily trained, which effectively increased the inter-class distance of the network extraction features and improved the accuracy of bird recognition. Compared with other weakly supervised methods such as RA-CNN, ST-CNN, and PDFR, our model achieved a better accuracy of 85.58% on the CUB200-2011. The proposed model not only used the label information of the species, but also constructed positive and negative sample pairs in the bird datasets, which exploited the unsupervised information. Experimental comparisons between our method and other existing approaches on two classification databases confirmed the superior classification accuracy of our approach. Moreover, our method also performed well on large-scale datasets. The model is generalizable, robust, and available for fine-grained image classification. In our future work, we will conduct the research on two directions. Firstly, we will find the solution on how to combine the the losses in different branches. Secondly, we will figure out how to integrate an attention mechanism with our model for more complex fine-grained categories.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Dataset** & **Acc** \\ \hline Our model (Xception+Bilinear+Triplet) & Birds-1096 & 88.91\% \\ Our model (Xception+Bilinear+Triplet) & CUB200-2011 & 85.58\% \\ Xception+Bilinear & Birds-1096 & 86.91\% \\ Xception+Bilinear & CUB200-2011 & 83.65\% \\ Xception & Birds-1096 & 79.68\% \\ Xception & CUB200-2011 & 71.38\% \\ RA-CNN (scale 1+2+3) & CUB200-2011 & 85.30\% \\ ST-CNN (Incaption net) & CUB200-2011 & 84.10\% \\ FDFR & CUB200-2011 & 82.60\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Accuracy comparisons of our model and other models. CNN: convolutional neural network.  
Figure 10: Acc curves of the evaluated different models trained on Birds-1096 and CUB200-2011.  
**Supplementary Materials:** The following are available at [[http://www.mdpi.com/2076-3417/8/10/1906/sl](http://www.mdpi.com/2076-3417/8/10/1906/sl)]([http://www.mdpi.com/2076-3417/8/10/1906/sl](http://www.mdpi.com/2076-3417/8/10/1906/sl)), File S1.  
**Author Contributions:** Conceptualization, Z.Z.; data curation, Z.Z.; formal analysis, Z.Z.; funding acquisition, Z.L.; investigation, J.L., K.W.; writing original draft, K.W.; review and editing, B.S.  
**Funding:** This research was supported by the IT integrated service platform of Sichuan Wolong Natural Reserve(STS-YS82E01), The National R&D Infrastructure and Facility Development Program of China, \"Fundamental Science Data Sharing Platform\" (DKA2018-12-02-XX), the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA19060205), the Special Project of Informatization of Chinese Academy of Sciences (XXH13505-03-205), the Special Project of Informatization of Chinese Academy of Sciences (XXH13506-305), the Special Project of Informatization of Chinese Academy of Sciences (XXH13506-303), and Around Five Top Priorities of \"One-Three-Five\" Strategic Planning, CNIC(Grant No. CNIC_PY-1408 and Grant No. CNIC_PY-1409).  
**Conflicts of Interest:** The authors declare no conflict of interest.