Fine-Grained Image Recognition from Click-Through Logs Using Deep Siamese Network

Abstract

Image recognition using deep network models has achieved remarkable progress in recent years. However, fine-grained recognition remains a big challenge due to the lack of large-scale well labeled dataset to train the network. In this paper, we study a deep network based method for fine-grained image recognition by utilizing the click-through logs from search engines. We use both click times and probability values to filter out the noise in click-through logs. Furthermore, we propose a deep siamese network model to fine-tune the classifier, emphasizing the subtle difference between different classes and tolerating the variation within the same class. Our method is evaluated by training with the Bing clickture-dog dataset and testing with the well labeled dog breed dataset. The results demonstrate great improvement achieved by our method compared with naive training.