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Perfect Snapping: An Accurate and Efficient Interactive Image segmentation Algorithm |
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PP: 1387-1393 |
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Author(s) |
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Qingsong Zhu,
Guanzheng Liu,
Zhanyong Mei,
Qi Li,
Yaoqin Xie,
Lei Wang,
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Abstract |
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| Interactive image segmentation is a process that extracts a foreground object from an image based on limited user input. In
this paper, we propose a novel interactive image segmentation algorithm named Perfect Snapping which is inspired by the presented
method named Lazy Snapping technique. In the algorithm, the mean shift algorithm with a boundary confidence prior is introduced to
efficiently pre-segment the original image into homogeneous regions (super-pixels) with precise boundary. Secondly, GaussianMixture
Model (GMM) clustering algorithm is used to describe and to model the super-pixels. Finally, a Monte Carlo based Expectation
Maximization (EM) algorithm is used to perform parametric learning of mixture model for priori knowledge. Experimental results
indicate that the proposed algorithm can achieve higher segmentation quality with higher efficiency. |
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