Interactive segmentation based on iterative learning for multiple-feature fusion

Lei Zhou, Yu Qiao, Yijun Li, Xiang Jian He, Jie Yang

Research output: Journal PublicationArticlepeer-review

6 Citations (Scopus)


This paper proposes a novel interactive segmentation method based on conditional random field (CRF) model to utilize the location and color information contained in user input. The CRF is configured with the optimal weights between two features, which are the color Gaussian Mixture Model (GMM) and probability model of location information. To construct the CRF model, we propose a method to collect samples for the cuttraining tasks of learning the optimal weights on a single image[U+05F3]s basis and updating the parameters of features. To refine the segmentation results iteratively, our method applies the active learning strategy to guide the process of CRF model updating or guide users to input minimal training data for training the optimal weights and updating the parameters of features. Experimental results show that the proposed method demonstrates qualitative and quantitative improvement compared with the state-of-the-art interactive segmentation methods. The proposed method is also a convenient tool for interactive object segmentation.

Original languageEnglish
Pages (from-to)240-252
Number of pages13
Publication statusPublished - 5 Jul 2014
Externally publishedYes


  • Active learning
  • Conditional random field
  • Interactive object segmentation
  • Parameter learning

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence


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