Open AccessSemantic image segmentation using an improved hierarchical graphical model
Author(s) -
Noormohamadi Neda,
Adibi Peyman,
Saeed Ehsani Sayyed Mohammad
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.2017.0738
Subject(s) - computer science , image segmentation , artificial intelligence , segmentation , image (mathematics) , scale space segmentation , computer vision , graphical model , pattern recognition (psychology) , segmentation based object categorization
Hierarchical graphical models can incorporate jointly several tasks in a unified framework. By applying this approach, information exchange among tasks would improve the results. A hierarchical conditional random field (CRF) is proposed here to improve the semantic image segmentation. Although this newly proposed model applies the information of several tasks, its run time is comparable with the contemporary approaches. This method is evaluated on MSRC dataset and has shown similar or better segmentation accuracy in comparison with models where CRFs or hierarchical models are adopted.