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Robust image segmentation against complex color distribution

机译:鲁棒的图像分割,防止复杂的色彩分布

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Color distribution is the most effective cue that is widely adopted in previous interactive image segmentation methods. However, it also may introduce additional errors in some situations, for example, when the foreground and background have similar colors. To address this problem, this paper proposes a novel method to learn the segmentation likelihoods. The proposed method is designed for high reliability, for which purpose it may choose to discard some unreliable likelihoods that may cause segmentation error. The reliability of likelihoods is estimated in a few Expectation-Maximization iterations. In each iteration, a novel multi-class transductive learning algorithm, namely, the Constrained Mapping, is proposed to learn likelihoods and identify unreliable likelihoods simultaneously. The resulting likelihoods then can be used as the input of any segmentation methods to improve their robustness. Experiments show that the proposed method is an effective way to improve both segmentation quality and efficiency, especially when the input image has complex color distribution.
机译:颜色分布是最有效的提示,已在以前的交互式图像分割方法中广泛采用。但是,在某些情况下(例如,前景和背景具有相似的颜色),它也会引入其他错误。为了解决这个问题,本文提出了一种学习分割可能性的新方法。所提出的方法是专为高可靠性而设计的,为此,它可以选择丢弃一些可能引起分割错误的不可靠可能性。在几次“期望最大化”迭代中估计了似然性的可靠性。在每次迭代中,提出一种新颖的多类跨导学习算法,即约束映射,以学习似然并同时识别不可靠的似然。然后,所得的可能性可用作任何分割方法的输入,以提高其鲁棒性。实验表明,该方法是一种提高分割质量和分割效率的有效方法,特别是在输入图像的颜色分布复杂的情况下。

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