Graph cut segmentation with a global constraint: Recovering region distribution via a bound of the Bhattacharyya measure
Graph cut segmentation with a global constraint: Recovering region distribution via a bound of the Bhattacharyya measure
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具有全局约束的图割分割：通过 Bhattacharyya 测度的界限恢复区域分布
DOI：
10.1109/cvpr.2010.5540045
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发表时间：
2010
期刊：
影响因子：
--
通讯作者：
S. Li
中科院分区：
文献类型：
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作者：
Ismail Ben Ayed;Hua;K. Punithakumar;Ian G. Ross;S. Li
研究了一种基于Bhattacharyya测度的全局约束图像分割算法。该问题包括找到一个区域与先验学习的图像分布一致。通过引入一个辅助标号，我们得到了Bhattacharyya测度的一个原始上界。从这个上界，我们重新制定的问题作为一个辅助功能的优化图切割。然后，我们证明了所提出的程序收敛，并给出了统计解释的上限。该算法只需要很少的迭代次数就能收敛，并且几乎可以找到全局最优解。定量评估和比较与国家的最先进的方法在Microsoft GrabCut分割数据库表明，该算法带来的分割精度，计算效率和最优性方面的改进。我们进一步证明了该算法在目标跟踪中的灵活性。
This study investigates an efficient algorithm for image segmentation with a global constraint based on the Bhattacharyya measure. The problem consists of finding a region consistent with an image distribution learned a priori. We derive an original upper bound of the Bhattacharyya measure by introducing an auxiliary labeling. From this upper bound, we reformulate the problem as an optimization of an auxiliary function by graph cuts. Then, we demonstrate that the proposed procedure converges and give a statistical interpretation of the upper bound. The algorithm requires very few iterations to converge, and finds nearly global optima. Quantitative evaluations and comparisons with state-of-the-art methods on the Microsoft GrabCut segmentation database demonstrated that the proposed algorithm brings improvements in regard to segmentation accuracy, computational efficiency, and optimality. We further demonstrate the flexibility of the algorithm in object tracking.