# Large-Scale Fine-Grained Bird Recognition Based on a Triplet Network and Bilinear Model
## 1 Introduction  
Image classification is a classical research topic in the computer vision field. Traditional image classification mainly categorizes semantic-level images or instance-level images. Semantic-level classification includes scene recognition [1; 2] and object recognition [3; 4], where the latter identifies different categories of objects such as cats and dogs. Meanwhile, instance-level classification distinguishes among individuals of an object, such as faces. Located between these two types, fine-grained image classification provides a more detailed class precision than coarse-grained image classification (such as object recognition), and detects subtle differences among the classes, often consisting of small local differences. For example, fine-grained classification distinguishes different types of birds [5], dogs [6], flowers [7], or any other object of interest. Fine-grained image classification is especially concerned with identifying the important distinguishing features. Such fine-grained features need to be tested against complex image backgrounds, and factors such as illumination, deformation, and occlusion interference with ambient noise should be reduced as far as possible [8]. The acquisition of fine-grained features is more complex than acquisition of coarse-grained features, and relies on image annotation to determine the complex parameters in the model while avoiding the over-fitting problem caused by small data amounts. Therefore, improving the extraction and choosing the appropriate structure of the convolutional neural network and the connection of the network are key initiatives. Fine-grained image classification can be divided into strongly supervised and weakly supervised approaches. In the strongly supervised approach, the category labels of the images during model training are supplemented by additional manual annotation information such as object-labelingboxes and part-labeling points. The authors of [9] detected the object levels and local areas in fine-grained images using a region-based convolutional neural network (R-CNN) algorithm. During the training phase, the R-CNN algorithm must mark the object frame and the part-labeling point. The object-labeling frame is also required in the test images. In [10], local areas were detected by a pose normalization algorithm. The images were cropped around the detected label box, and the local information at different levels was extracted for pose alignment. Finally, the convolution characteristics of the different layers were obtained. The model was constructed from two modules: one for local positioning, the other for feature learning of the global and local image blocks. However, the practicality of pose normalization is limited by the high expense of acquiring the annotation information. Weakly supervised fine-grained image classification uses labels alone, without requiring additional annotation information. As local-area information is essential in fine-grained image classification, the detection of local areas would improve the performance of weakly supervised fine-grained image classification. The first algorithm with no reliance on annotation information was proposed in [11]. This algorithm uses two levels of features--object-level features and local-level features--and completes the fine-grained image classification by using category labels. The authors of [12] extracted local-area information based on several essential points derived from the CNN features [13]. A bilinear CNN model that performs local-area detection and feature extraction using two networks was proposed in [14]. Birds are an important part of natural ecosystem. Correct recognition of birds is very important for the protection and research of birds. However, classifying birds correctly is very difficult because the differences between the different species are very subtle. Therefore, our paper focuses on the fine-grained identification of birds. We propose a network based on the triplet network and the bilinear model.