25 research outputs found
Seeing Behind the Camera: Identifying the Authorship of a Photograph
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.Comment: Dataset downloadable at http://www.cs.pitt.edu/~chris/photographer To
Appear in CVPR 201
Targeted Background Removal Creates Interpretable Feature Visualizations
Feature visualization is used to visualize learned features for black box
machine learning models. Our approach explores an altered training process to
improve interpretability of the visualizations. We argue that by using
background removal techniques as a form of robust training, a network is forced
to learn more human recognizable features, namely, by focusing on the main
object of interest without any distractions from the background. Four different
training methods were used to verify this hypothesis. The first used unmodified
pictures. The second used a black background. The third utilized Gaussian noise
as the background. The fourth approach employed a mix of background removed
images and unmodified images. The feature visualization results show that the
background removed images reveal a significant improvement over the baseline
model. These new results displayed easily recognizable features from their
respective classes, unlike the model trained on unmodified data