  We address the problem of contour detection via per-pixel classifications of
edge point. To facilitate the process, the proposed approach leverages with
DenseNet, an efficient implementation of multiscale convolutional neural
networks (CNNs), to extract an informative feature vector for each pixel and
uses an SVM classifier to accomplish contour detection. The main challenge lies
in adapting a pre-trained per-image CNN model for yielding per-pixel image
features. We propose to base on the DenseNet architecture to achieve pixelwise
fine-tuning and then consider a cost-sensitive strategy to further improve the
learning with a small dataset of edge and non-edge image patches. In the
experiment of contour detection, we look into the effectiveness of combining
per-pixel features from different CNN layers and obtain comparable performances
to the state-of-the-art on BSDS500.
