Enhanced decision fusion of semantically segmented images via local majority saliency map

Abstract

Decision fusion is the most important step in ensemble machine learning schemes. One of the greatest challenges of decision fusion is the discrete nature of decisions. This challenge causes decision fusion solutions to become variations of the voting algorithm from statistical perspective. However, increasing the redundancy of decisions imposes a serious computational challenge for real‐time systems. Resorting to fewer decisions imposes uncertainty challenges. In this Letter, the authors present a methodology to generate saliency maps for decision fusion. Specifically, they propose a local saliency map for decision fusion using a local majority filter. They choose semantic segmentation via pixel labelling produced from a random decision forest model as a case study. The local saliency map is used to derive three intermediate labelled images that are added to the voting pool and hence rectifying the final decision. The results of the proposed solution reduced the error by 26% and increased robustness by 16% with only two decisions.