# Bilinear CNN Model for Fine-Grained Classification Based on Subcategory-Similarity Measurement
## 5 Discussion  
Aiming at the disadvantages of B-CNN, a fine-grained classification method based on image localization and subcategory-similarity measurement, is proposed. The method incorporates the advantages of weakly supervised localization, the excellent feature-extraction ability of B-CNN, and the measurement of subcategory similarities. The improved triplet-loss function and weighted  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Annotation** & **Accuracy** \\ \hline SPDA-CNN & \(\surd\) & 84.55\% \\ PN-CNN & \(\surd\) & 85.40\% \\ B-CNN & \(\surd\) & 84.80\% \\ TSC & \(-\) & 84.69\% \\ LRBP & \(-\) & 84.21\% \\ HIHCA & \(-\) & 85.30\% \\ \(\alpha\) pooling & \(-\) & 85.30\% \\ MA-CNN & \(-\) & **86.50\%** \\ B-CNN & \(-\) & 84.00\% \\ Ours & \(-\) & 85.31\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison with previous methods on CUB-200-2011.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Annotation** & **Accuracy** \\ \hline FCAN & \(\surd\) & 91.30\% \\ PA-CNN & \(\surd\) & 92.60\% \\ B-CNN & \(-\) & 91.20\% \\ Improved B-CNN & \(-\) & 92.00\% \\ LRBP & \(-\) & 90.92\% \\ HIHCA & \(-\) & 91.70\% \\ Kernel Pooling & \(-\) & 92.40\% \\ RA-CNN & \(-\) & 92.50\% \\ MA-CNN & \(-\) & **92.80\%** \\ Ours & \(-\) & 92.43\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison with previous methods on Cars-196.  
softmax-loss function were combined to restrict interclass distance and increase intraclass distance. Experimental results show that our method can distinguish some subcategories with similar appearance, and outperforms several state-of-the-art weakly supervised classification models. Discriminative feature representation from this model by jointly optimizing these two types of losses accelerates the convergence of the learning. This can be employed for various tasks, such as image classification, verification, and retrieval. However, due to incorrect labels in the dataset itself and some subcategories that cannot be distinguished only from appearance (for example, some subcategories could only be distinguished by habitat and voice rather than appearance in CUB-200-2011), our method had a higher classification error rate for these samples. For future work, we will include descriptors' weights to precisely locate the whole object and train the asymmetric B-CNN to extract more abundant features.  
conceptualization, X.D., and S.G.; methodology, X.D.; software, X.D.; validation, X.D., and S.Z.; formal analysis, X.D.; investigation, X.D.; resources, S.G.; data curation, X.D.; writing--original draft preparation, X.D.; writing--review and editing, X.D.; visualization, X.D.; supervision, S.Z. and Z.B.; project administration, S.G.; funding acquisition, S.G., and S.Z.  
This research was funded by the National Natural Science Foundation of China (NSFC Grant No. 61702055), the Provincial Natural Science Foundation of Jiangsu (Grant No. BK20151254, BK20151260), and the Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University (Grant No. 93K172017K18).  
The authors declare no conflict of interest.