  We address the problem of estimating image difficulty defined as the human
response time for solving a visual search task. We collect human annotations of
image difficulty for the PASCAL VOC 2012 data set through a crowd-sourcing
platform. We then analyze what human interpretable image properties can have an
impact on visual search difficulty, and how accurate are those properties for
predicting difficulty. Next, we build a regression model based on deep features
learned with state of the art convolutional neural networks and show better
results for predicting the ground-truth visual search difficulty scores
produced by human annotators. Our model is able to correctly rank about 75%
image pairs according to their difficulty score. We also show that our
difficulty predictor generalizes well to new classes not seen during training.
Finally, we demonstrate that our predicted difficulty scores are useful for
weakly supervised object localization (8% improvement) and semi-supervised
object classification (1% improvement).
