Image semantic segmentation based on FCN-CRF model

Image segmentation is a key point for analyzing and understanding image, which occupies an important position in image processing. Recent studies have attempted to tackle pixel level labeling tasks using deep learning. In our paper, we propose an approach of combining fully convolutional network and conditional random field for image semantic segmentation. We utilize FCN model to automatically learn features directly from original image data, and create local predictions and global structure consistency by combining fine layers and coarse layers. CRF is a probabilistic graph and used to fully exploit the context information. Our model train the whole deep network end-to-end with the back-propagation algorithm and maximum likelihood estimation. The key of jointing FCN and CRF is sensitivity of neurons, calculating the sensitivity by CRF and then transferring it to FCN. Experiments show our method achieves improved accuracy compared with several other methods on Pascal VOC 2012 dataset.