Open AccessImage segmentation fusion using weakly supervised trace‐norm multi‐task learning method
Author(s) -
Liang Xianpeng,
Huang DeShuang
Publication year - 2018
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2017.1061
Subject(s) - artificial intelligence , segmentation , computer science , benchmark (surveying) , image segmentation , pattern recognition (psychology) , fuse (electrical) , image fusion , fusion , scale space segmentation , image (mathematics) , computer vision , machine learning , linguistics , philosophy , engineering , geodesy , electrical engineering , geography
In this study, the authors propose a new method to fuse multiple segmentations generated by different methods or same methods with different parameters. The proposed method has several contributions. First, they convert the image segmentation fusion problem into a weakly supervised learning problem. Thus, the information of superpixels can be used to guide the fusion process. Second, they treat the multiple segmentations as multiple closely related tasks and utilise multi‐task learning method to evaluate the reliability of the segmentations. Third, they design a strategy to ensemble the evaluated segmentation maps to obtain the final segmentation. The experiment on the benchmark dataset MSRC demonstrates the superior performance of the proposed method on image foreground and background segmentations.