Seeing Behind the Camera: Identifying the Authorship of a Photograph
This addresses a new authorship identification problem in computer vision, but it is incremental as it applies existing techniques to a new dataset.
The paper tackles the novel problem of identifying the photographer behind a photograph by creating a dataset of over 180,000 images from 41 photographers and testing various features, including a new deep CNN, finding that high-level features outperform low-level ones.
We introduce the novel problem of identifying the photographer behind a photograph. To explore the feasibility of current computer vision techniques to address this problem, we created a new dataset of over 180,000 images taken by 41 well-known photographers. Using this dataset, we examined the effectiveness of a variety of features (low and high-level, including CNN features) at identifying the photographer. We also trained a new deep convolutional neural network for this task. Our results show that high-level features greatly outperform low-level features. We provide qualitative results using these learned models that give insight into our method's ability to distinguish between photographers, and allow us to draw interesting conclusions about what specific photographers shoot. We also demonstrate two applications of our method.