Abstract
In our findings we try to explore spatial context to obtained good results of semantic segmentation. Spatial context has patch-to-patch and patch-to-background. Patch-to-patch context has semantic relationships on visual patterns of two stuffs of a image. Patch-to-background context had semantic relations in image patch and whole background region. In our research we have explored contextual relations based on CRF. CNNs pair-wise potential captures semantic correlation on nearby patches. Researchers in the past used CNN-Sparse CRF.In our model we used CNN-Dense CRF technique to refine our samples to sharpen the object boundaries. CNN-Dense CRF use pair-wise potentials for local smoothness of images. PairWise potentials are log-linear functions for semantic compatibility in image regions. CRF Pairwise is to develop coarse-level prediction. CRF and Potts-model-based pair-wise potential are jointed to obtained good results for semantic segmentation. Keywords: Deep Learning, FCNN, ANN, Adaboost, CRF, SS-Semantic Segmentation.