Contour Detection Using Cost-Sensitive Convolutional Neural Networks
This work addresses contour detection in computer vision, which is incremental as it adapts existing methods with cost-sensitive learning for small datasets.
The paper tackles contour detection by using a cost-sensitive convolutional neural network with DenseNet for per-pixel feature extraction and an SVM classifier, achieving comparable performance to state-of-the-art methods on the BSDS500 dataset.
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.