Interactive segmentation with recommendation of most informative regions

Authors:
Canxiang Yan;Dan Wang;Shiguang Shan;Xilin Chen
Affiliations:
Key Laboratory of Intelligent Information, Processing of Chinese Academy of Sciences(CAS), Institute of Computing Technology, Beijing, China;Key Laboratory of Intelligent Information, Processing of Chinese Academy of Sciences(CAS), Institute of Computing Technology, Beijing, China;Key Laboratory of Intelligent Information, Processing of Chinese Academy of Sciences(CAS), Institute of Computing Technology, Beijing, China;Key Laboratory of Intelligent Information, Processing of Chinese Academy of Sciences(CAS), Institute of Computing Technology, Beijing, China
Venue:
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Year:
2012

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Abstract

Compared to automatic segmentation, interactive segmentation is a flexible method to separate the interesting object from background. However, satisfactory results may not be achieved even with lots of interactions since user's operation may not provide enough information to decide the labels of ambiguous regions. To deal with this problem, we present an interactive segmentation approach based on active learning scheme, which can automatically recommend the most informative regions to guide the user interactions. Our method employs a two-step strategy. Firstly, based on initial user interactions, it adopts active learning to iteratively select the most crucial regions and query the oracle for their true labels. In the second step, we minimize an energy function, which combines low-level features extracted from total interactions, to segment the object. Experimental results demonstrate our method can achieve high segmentation accuracy within desirable interactions.