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.
CITATION STYLE
Feng, W., & Liu, D. (2017). Fine-grained image recognition from click-through logs using deep siamese network. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10132 LNCS, pp. 127–138). Springer Verlag. https://doi.org/10.1007/978-3-319-51811-4_11
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