Abstract
Interactive image matting 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 matting 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, Gaussian Mixture 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 matting quality with higher efficiency.
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© 2013 Springer-Verlag Berlin Heidelberg
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Zhu, Q., Shao, L., Li, Q., Xie, Y. (2013). Perfect Snapping. In: Li, S., et al. Advances in Multimedia Modeling. Lecture Notes in Computer Science, vol 7733. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35728-2_8
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DOI: https://doi.org/10.1007/978-3-642-35728-2_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35727-5
Online ISBN: 978-3-642-35728-2
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