Research on Fine-grained Image Classification Based on Pattern Mining

• 1、Department of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044

Abstract：Since subtle visual differences among fine-grained subcategories only locate at local regions, part localization is a key issue for fine-grained image classification. This paper proposes a method based on pattern mining, named UPM (Unsupervised Part Mining), which can localize discriminative parts under a fully-unsupervised setting. Firstly, the pattern mining algorithm is utilized to mine visual patterns in the feature maps extracted from a pre-trained convolutional neural network model. Inspired by the fact that these relevant meaningful patterns typically hold appearance and spatial consistency, then the clustering algorithm is performed on mined patterns to obtain the discriminative parts. Finally, a multi-stream classification model is built, which aggregates the image-level, object-level and part-level features simultaneously for classification. The UPM approach does not need any supervision, but can accurately localize the discriminative parts in the fine-grained image only relying on pre-trained CNN model. Experiments show that the UPM approach not only achieves the competitive performance, but also reduces the training time of the model.

Keywords： Fine-grained image classification pattern mining unsupervised localization deep learning