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首页> 外文期刊>IEEE transactions on multimedia >Interactive Multilabel Image Segmentation via Robust Multilayer Graph Constraints
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Interactive Multilabel Image Segmentation via Robust Multilayer Graph Constraints

机译：通过稳健的多层图约束进行交互式多标签图像分割

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摘要

The combination of pixel and superpixel has been widely utilized in the interactive segmentation methods to overcome the sensitivity to the seeds’ quantity and quality. However, because of the introduction of more variables and variables’ interactions, the pixel–superpixel combination methods are still limited to the segmentation accuracy and computational complexity. To solve these problems, in this paper, we propose an interactive multilabel image segmentation method. In the proposed segmentation model, the multilayer relationships among the pixel layer, superpixel layer, and label layer are fused by the Markov random field framework to further improve the segmentation accuracy. During the optimization stage, the parallel partial optimality strategy is utilized to effectively solve the multilabel submodular energy function. Experimental results on challenging data sets demonstrate the competitiveness of the proposed method comparing with several state-of-the-art interactive algorithms.
机译：像素和超像素的组合已广泛用于交互式分割方法中，以克服对种子数量和质量的敏感性。但是，由于引入了更多的变量和变量之间的相互作用，因此像素-超像素组合方法仍然局限于分割精度和计算复杂度。为了解决这些问题，本文提出了一种交互式的多标签图像分割方法。在提出的分割模型中，通过马尔可夫随机场框架融合了像素层，超像素层和标签层之间的多层关系，进一步提高了分割精度。在优化阶段，利用并行局部最优策略有效地解决了多标签亚模能量函数。具有挑战性的数据集的实验结果证明了该方法与几种最新的交互式算法相比具有竞争力。

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