  In recent years, many publications showed that convolutional neural network
based features can have a superior performance to engineered features. However,
not much effort was taken so far to extract local features efficiently for a
whole image. In this paper, we present an approach to compute patch-based local
feature descriptors efficiently in presence of pooling and striding layers for
whole images at once. Our approach is generic and can be applied to nearly all
existing network architectures. This includes networks for all local feature
extraction tasks like camera calibration, Patchmatching, optical flow
estimation and stereo matching. In addition, our approach can be applied to
other patch-based approaches like sliding window object detection and
recognition. We complete our paper with a speed benchmark of popular CNN based
feature extraction approaches applied on a whole image, with and without our
speedup, and example code (for Torch) that shows how an arbitrary CNN
architecture can be easily converted by our approach.
