关键词: 图像识别, 小样本, 卷积神经网络, 原型网络, 嵌入空间, 数据增强

Abstract: In the current image recognition field, most of the classification or recognition methods are built on the basis of existing large amounts of data, which are put into training and classified through sampling analysis and feature extraction. However, in the real world, most target classification problems do not have a large amount of annotated data. In order to solve the problem of image recognition based on small data sets, this paper uses the data augmentation to enhance data sets, and uses multi-layer CNN to map the image into high-dimensional space, then gets prototypes of each class by using the prototype network. Finally, the test image can be classified according to the distance among prototype points and test image in the embedded space. Experimental results show that this method has high recognition accuracy under the condition of small data set, and also has good stability and strong robustness.

Key words: image recognition, small data set, CNN, prototype network, embedding space, data augmentation