Interactive segmentation based on iterative learning for multiple-feature fusion

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'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. HighlightsAn iterative interactive segmentation method is proposed.A strategy for learning CRF parameters on a single image's basis.A strategy for updating the parameters of features iteratively.The CRF model is updated adaptively using active learning strategy.Users are guided to input new scribbles to improve the segmentation accuracy.