DenseCut: Densely Connected CRFs for Realtime GrabCut
DenseCut: Densely Connected CRFs for Realtime GrabCut
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DOI：
10.1111/cgf.12758
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发表时间：
2015-10
影响因子：
2.5
通讯作者：
Ming-Ming Cheng-Ming;V. Prisacariu;Shuai Zheng;Philip H. S. Torr;C. Rother
中科院分区：
文献类型：
--
作者：
Ming-Ming Cheng-Ming;V. Prisacariu;Shuai Zheng;Philip H. S. Torr;C. Rother
自动或手动提供的边界框输入的图形背景分割在过去十年中非常流行，并影响了各种应用。许多研究都集中在高质量的分割上，使用复杂的公式，这通常会导致技术缓慢，并且常常妨碍实际使用。在本文中，我们展示了一种非常快速的分割技术，该技术仍然可以获得非常高质量的结果。我们建议用密集连接的 CRF 取代传统 GrabCut 公式中耗时的全局颜色模型迭代细化。为了推动这一决定，我们证明了密集的 CRF 隐式地模拟了前景和背景的非标准化全局颜色模型。这种关系为密集 crf 和 GrabCut 泛函之间的桥梁提供了富有洞察力的分析。我们使用两个著名的基准测试来广泛评估我们的算法。我们的实验结果表明，所提出的算法相对于最接近的竞争对手实现了一个数量级（10×）的加速，同时实现了相当高的精度。
Figure‐ground segmentation from bounding box input, provided either automatically or manually, has been extremely popular in the last decade and influenced various applications. A lot of research has focused on high‐quality segmentation, using complex formulations which often lead to slow techniques, and often hamper practical usage. In this paper we demonstrate a very fast segmentation technique which still achieves very high quality results. We propose to replace the time consuming iterative refinement of global colour models in traditional GrabCut formulation by a densely connected crf. To motivate this decision, we show that a dense crf implicitly models unnormalized global colour models for foreground and background. Such relationship provides insightful analysis to bridge between dense crf and GrabCut functional. We extensively evaluate our algorithm using two famous benchmarks. Our experimental results demonstrated that the proposed algorithm achieves an order of magnitude (10×) speed‐up with respect to the closest competitor, and at the same time achieves a considerably higher accuracy.