Applied Mathematics & Information Sciences

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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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