A variational model and graph cuts optimization for interactive foreground extraction

Authors:
Liman Liu;Wenbing Tao;Jin Liu;Jinwen Tian
Affiliations:
Institute for Pattern Recognition and Artificial Intellegence, Huazhong University of Science and Technology, Wuhan 430074, China;School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China;School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China;Institute for Pattern Recognition and Artificial Intellegence, Huazhong University of Science and Technology, Wuhan 430074, China
Venue:
Signal Processing
Year:
2011

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Abstract

In this paper, an interactive segmentation method is proposed, which is based on an improved Chan-Vese model, i.e. multiple piecewise constant model with geodesic active contour. The k-means method is used to learn the models of the foreground and background, which are the optimal piecewise constant approximation of the original image according to the input seeds clue by the user. Based on the piecewise constant models of the foreground and background, the multiple piecewise constant with a geodesic active contour energy function can be minimized by effective graph cuts algorithm, and the minimum cuts can be used to partition the image into the foreground and background. Numerical experiments demonstrate the superior performance of the proposed interactive foreground extraction method based on the improved Chan-Vese model compared to the original Chan-Vese model by simple user interaction.