Multi-hypothesis contextual modeling for semantic segmentation

•We explore contextual models for fusion of alternative segmentations of the image.•Contextual constraints are defined on intersecting superpixels from multiple segmentations.•This multi-hypothesis MRF model improves the labeling accuracy of tested methods.•When used after FCN and PSP segmentations, the model achieves state-of-the-art results. Semantic segmentation (i.e. image parsing) aims to annotate each image pixel with its corresponding semantic class label. Spatially consistent labeling of the image requires an accurate description and modeling of the local contextual information. Segmentation result is typically improved by Markov Random Field (MRF) optimization on the initial labels. However this improvement is limited by the accuracy of initial result and how the contextual neighborhood is defined. In this paper, we develop generalized and flexible contextual models for segmentation neighborhoods in order to improve parsing accuracy. Instead of using a fixed segmentation and neighborhood definition, we explore various contextual models for fusion of complementary information available in alternative segmentations of the same image. In other words, we propose a novel MRF framework that describes and optimizes the contextual dependencies between multiple segmentations. Simulation results on two common datasets demonstrate significant improvement in parsing accuracy over the baseline approaches.