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首页> 外文期刊>Multimedia, IEEE Transactions on >Unsupervised Salient Object Segmentation Based on Kernel Density Estimation and Two-Phase Graph Cut
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Unsupervised Salient Object Segmentation Based on Kernel Density Estimation and Two-Phase Graph Cut

机译：基于核密度估计和两相图割的无监督显着目标分割

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摘要

In this paper, we propose an unsupervised salient object segmentation approach based on kernel density estimation (KDE) and two-phase graph cut. A set of KDE models are first constructed based on the pre-segmentation result of the input image, and then for each pixel, a set of likelihoods to fit all KDE models are calculated accordingly. The color saliency and spatial saliency of each KDE model are then evaluated based on its color distinctiveness and spatial distribution, and the pixel-wise saliency map is generated by integrating likelihood measures of pixels and saliency measures of KDE models. In the first phase of salient object segmentation, the saliency map based graph cut is exploited to obtain an initial segmentation result. In the second phase, the segmentation is further refined based on an iterative seed adjustment method, which efficiently utilizes the information of minimum cut generated using the KDE model based graph cut, and exploits a balancing weight update scheme for convergence of segmentation refinement. Experimental results on a dataset containing 1000 test images with ground truths demonstrate the better segmentation performance of our approach.
机译：在本文中，我们提出了一种基于核密度估计（KDE）和两阶段图割的无监督显着目标分割方法。首先基于输入图像的预分割结果构建一组KDE模型，然后针对每个像素，计算出适合所有KDE模型的一组似然度。然后根据每个KDE模型的颜色独特性和空间分布来评估其颜色显着性和空间显着性，并通过整合像素的似然性度量和KDE模型的显着性度量来生成逐像素显着性图。在显着对象分割的第一阶段，利用基于显着性图的图割获得初始分割结果。在第二阶段中，基于迭代种子调整方法进一步细分细分，该方法有效利用了利用基于KDE模型的图切割生成的最小切割信息，并采用了平衡权重更新方案来实现细分优化的收敛。在包含具有基本事实的1000张测试图像的数据集上的实验结果表明，我们的方法具有更好的分割效果。

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