Semantic Segmentation via Structured Patch Prediction, Context CRF and
Guidance CRF
Abstract
This paper describes a fast and accurate semantic image
segmentation approach that encodes not only segmentationspecified features but also high-order context compatibilities and boundary guidance constraints. We introduce a
structured patch prediction technique to make a trade-off
between classification discriminability and boundary sensibility for features. Both label and feature contexts are
embedded to ensure recognition accuracy and compatibility, while the complexity of the high order cliques is reduced by a distance-aware sampling and pooling strategy. The proposed joint model also employs a guidance CRF to further enhance the segmentation performance. The
message passing step is augmented with the guided filtering which enables an efficient and joint training of the whole system in an end-to-end fashion. Our proposed joint model outperforms the state-of-art on Pascal VOC 2012
and Cityscapes, with mIoU(%) of 82.5 and 79.2 respectively. It also reaches a leading performance on ADE20K,
which is the dataset of the scene parsing track in ILSVRC
2016. The code is available at https://github.com/
FalongShen/SegModel.