Enhanced Figure-Ground Classification With Background Prior Propagation
Enhanced Figure-Ground Classification With Background Prior Propagation
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DOI：
10.1109/tip.2015.2389612
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发表时间：
2015-01
影响因子：
10.6
通讯作者：
Yisong Chen;Antoni B. Chan
中科院分区：
文献类型：
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作者：
Yisong Chen;Antoni B. Chan
我们提出了一种自适应图形-背景分割算法，能够在通用环境中提取前景对象。从交互式分配的背景掩模开始，定义初始背景先验，并通过渐进式补丁合并从不同的前景先验生成多个软标签分区。这些分区被融合以产生前景概率图。然后通过阈值扫描将概率图二值化以创建多个硬标签候选。使用不同的评估分数形成一组分割假设。从该集合中，具有最大局部稳定性的假设被传播为新的背景先验，并且重复分割过程直到收敛。使用相似性投票来选择获胜者集，并融合相应的假设以产生最终的分割结果。实验表明，我们的方法在多个数据集上的表现达到或超过了当前最先进的水平，尤其是在包含不规则或多连接前景的挑战性场景上取得了特别成功。
We present an adaptive figure-ground segmentation algorithm that is capable of extracting foreground objects in a generic environment. Starting from an interactively assigned background mask, an initial background prior is defined and multiple soft-label partitions are generated from different foreground priors by progressive patch merging. These partitions are fused to produce a foreground probability map. The probability map is then binarized via threshold sweeping to create multiple hard-label candidates. A set of segmentation hypotheses is formed using different evaluation scores. From this set, the hypothesis with maximal local stability is propagated as the new background prior, and the segmentation process is repeated until convergence. Similarity voting is used to select a winner set, and the corresponding hypotheses are fused to yield the final segmentation result. Experiments indicate that our method performs at or above the current state-of-the-art on several data sets, with particular success on challenging scenes that contain irregular or multiple-connected foregrounds.