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Superpixel-Based Foreground Extraction With Fast Adaptive Trimaps
IEEE Transactions on Cybernetics ( IF 9.4 ) Pub Date : 2017-09-12 , DOI: 10.1109/tcyb.2017.2747143
Xuelong Li , Kang Liu , Yongsheng Dong

Extracting the foreground from a given complex image is an important and challenging problem. Although there have been many methods to perform foreground extraction, most of them are time-consuming, and the trimaps used in the matting step are labeled manually. In this paper, we propose a fast interactive foreground extraction method based on the superpixel GrabCut and image matting. Specifically, we first extract superpixels from a given image and apply GrabCut on them to obtain a raw mask. Due to that the resulting mask border is hard and toothing, we further propose fast and adaptive trimaps (FATs), and construct an FATs-based shared matting for computing a refined mask. Finally, by interactive processing, we can obtain the final foreground. Experimental results on the BSDS500 and alphamatting datasets demonstrate that our proposed method is faster than five representative methods, and performs better than the interactive representative methods in terms of the three evaluation criteria: 1) mean square error; 2) sum of absolute difference; and 3) execution time.

中文翻译:


使用快速自适应三元图进行基于超像素的前景提取



从给定的复杂图像中提取前景是一个重要且具有挑战性的问题。尽管已经有很多方法来执行前景提取,但大多数方法都很耗时,并且抠图步骤中使用的三元图是手动标记的。在本文中,我们提出了一种基于超像素 GrabCut 和图像抠图的快速交互式前景提取方法。具体来说,我们首先从给定图像中提取超像素,并对它们应用 GrabCut 以获得原始掩模。由于得到的掩模边界是硬的和有齿的,我们进一步提出快速和自适应三元图(FAT),并构建基于FAT的共享抠图来计算精细掩模。最后通过交互处理,我们就可以得到最终的前景。 BSDS500 和 alphamatting 数据集上的实验结果表明,我们提出的方法比五种代表性方法更快,并且在三个评估标准方面比交互式代表性方法表现更好:1)均方误差; 2)绝对差之和; 3)执行时间。
更新日期:2017-09-12
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