Deep Convolutional Networks for Scene Parsing
暂无分享,去创建一个
We propose a deep learning strategy for scene parsing, i.e. to asssign a class label to each pixel of an image. We investigate the use of deep convolutional network for modeling the complex scene label structures, relying on a supervised greedy learning strategy. Compared to standard approaches based on CRFs, our strategy does not need hand-crafted features, allows modeling more complex spatial dependencies and has a lower inference cost. Experiments over the MSRC benchmark and the LabelMe dataset show the effectiveness of our approach.
[1] Yoshua Bengio,et al. Convolutional networks for images, speech, and time series , 1998 .
[2] Yoshua Bengio,et al. Greedy Layer-Wise Training of Deep Networks , 2006, NIPS.
[3] Bill Triggs,et al. Scene Segmentation with CRFs Learned from Partially Labeled Images , 2007, NIPS.
[4] Antonio Criminisi,et al. Object Class Segmentation using Random Forests , 2008, BMVC.