Semantic Segmentation in Video
We explore the efficiency of the Conditional Random Field (CRF) based inference for semantic segmentation in videos. The key idea is to combine best of the two worlds – semantic co-labeling and more expressive models. We follow the mean-field updates for higher order clique potentials and extend the spatial smoothness and appearance kernels to address co-segmentation of multiple frames of a video. Thus the system becomes amenable to perform video semantic segmentation effectively.