Abstract the GrabCut can effectively extract the foreground according to features in a cartoon image; however, the performance is not so effective for a real image, because the feature extraction is independent of segmentation. To improve segmentation performance, this paper proposes an improved GrabCut which combines the segmentation and multiscale feature extraction into a unified model. In this model, the segmentation relies on multiscale features, and the multiscale features depend on multiscale decomposition. A novel total variation regularization is proposed in multiscale decomposition to preserve edges and remove the region inhomogeneity, by which the generalization of features for segmentation is improved. The features obtained by the multiscale decomposition are integrated into the segmentation process, and the foreground can be easily extracted from a proper scale. Experimental results indicate that, compared to the existing GrabCut and improved techniques, this method provides competitive performance in terms of the segmentation accuracy, while being insensitive to inhomogeneity.

中文翻译：

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多尺度特征的改进 GrabCut

Abstract GrabCut可以根据卡通图像的特征有效提取前景；然而，对于真实图像，性能并不是那么有效，因为特征提取与分割无关。为了提高分割性能，本文提出了一种改进的 GrabCut，它将分割和多尺度特征提取结合到一个统一的模型中。在这个模型中，分割依赖于多尺度特征，多尺度特征依赖于多尺度分解。在多尺度分解中提出了一种新的全变分正则化，以保留边缘并去除区域不均匀性，从而改进了分割特征的泛化。将多尺度分解得到的特征融入到分割过程中，并且可以轻松地从适当的比例中提取前景。实验结果表明，与现有的 GrabCut 和改进技术相比，该方法在分割精度方面提供了有竞争力的性能，同时对不均匀性不敏感。