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

Content Provider	IET Digital Library
Author	Hossny, M. Nahavandi, S. Creighton, D. Lim, C. Bhatti, A.
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.
Starting Page	1036
Ending Page	1038
Page Count	3
ISSN	00135194
Volume Number	53
e-ISSN	1350911X
Issue Number	Issue 15, Jul (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/el/53/15
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/el.2016.4709
Journal	Electronics Letters
Publisher Date	2017-06-20
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Computer Vision And Image Processing Technique Decision Redundancy Enhanced Decision Fusion Ensemble Machine Learning Scheme Image Fusion Image Segmentation Intermediate Labelled Image Knowledge Engineering Technique Learning in AI Local Majority Filter Local Majority Saliency Map Optical, Image And Video Signal Processing Pixel Labelling Random Decision Forest Model Real Time System Saliency Map Generation Semantically Segmented Image Sensor Fusion Statistical Analysis Statistical Perspective Statistics Uncertainty Challenges Voting Algorithm Voting Pool
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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