# Bilinear CNN Model for Fine-Grained Classification Based on Subcategory-Similarity Measurement
## 1 Introduction  
Fine-grained image classification is a challenging task in computer vision. Different from general object classification that aims to distinguish basic-level categories, fine-grained image classification focuses on recognizing images that belong to the same basic category, but not the same class or subcategory, such as bird species, dog breeds, and car types. The classification process is more challenging due to subtle interclass variances and large intraclass differences between different subcategories [1, 2]. With the development of deep learning [3, 4, 5], convolutional neural networks (CNNs) have recently produced remarkable results in image representation learning in image classification. Inspired by past developments in handcrafted features, many CNN-based fine-grained classification approaches have been proposed that benefit a wide variety of application scenarios in both industry and research, such as image retrieval, wildlife protection, and medical-image analysis [6].  
The main challenge of fine-grained classification is that the differences between different subcategories are usually subtle and local, so how to locate discriminative regions has become a hot topic. According to whether additional annotations, such as bounding box and location information of local regions, are used in the training stage, we can roughly divide the CNN-based methods into two groups: strongly supervised methods [7, 8, 9, 10, 11] and weakly supervised methods [12, 13, 14]. The strongly supervised methods mainly include two steps: firstly, the object or part(s) is located with the help of additional annotations, and discriminative features are extracted for further classification. Since the acquisition of additional annotations is significantly labor-consuming, and the preannotated components may not be the most appropriate choice for the final classification task, weakly supervisedclassification methods using only category labels have been widely studied, and B-CNN is one of the most classical methods. Motivated by the excellent feature-extraction ability of B-CNN, this paper chose it as the basic model to extract features. To eliminate the influence of background noise of the input image, we first carried out weakly supervised localization of the input image to obtain more accurate features, which directly obtain the foreground of the original image under the weakly supervised setting rather than detecting and locating local regions.  
For fine-grained image classification, similarities between different subcategories are different. As shown in Figure 1, the top row are three Audi subcategories with similar colors and perspectives in Stanford Cars-196 [15], but with different combinations of make and model. On the other hand, the second row shows three images of Audi R8. The visual variances within Audi R8 are much greater than those between subcategories in the top row, which means that the similarity between Audi S4 and TT is much higher than that between S4 and R8. Focused on the issue that existing algorithms treat all subcategories by equal cost, which limits the classification ability of subcategories with significant similarity, a bilinear CNN fine-grained image-classification method based on subcategory similarity is proposed.  
The rest of this article is organized as follows: Section 2 reviews the related work. Section 3 introduces our approach in detail, and Section 4 presents the experiments as well as the result analysis. Finally, conclusion and future work are given in Section 5.