Image segmentation based on the hybrid total variation model and the K-means clustering strategy
Image segmentation based on the hybrid total variation model and the K-means clustering strategy
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DOI：
10.3934/ipi.2016022
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发表时间：
2016-05
期刊：
影响因子：
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通讯作者：
Baoli Shi;Z. Pang;Jing Xu
中科院分区：
文献类型：
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作者：
Baoli Shi;Z. Pang;Jing Xu
图像分割的性能高度依赖于原始输入图像。当图像被噪声或模糊污染时，采用直接分割方法无法获得有效的分割结果。为了有效分割污染图像，本文提出了一种基于盒形约束的混合全变分模型和k均值聚类方法的两步分割方法。第一步，混合模型基于总变分函数与高阶总变分函数之间的加权凸组合作为正则化项，得到原始聚类数据；为了处理非光滑正则化项，我们采用交替分裂Bregman方法对该模型进行求解。然后，在第二步中，将聚类数据阈值分割成不同的阶段，其中阈值可以使用K-means聚类方法给出。数值比较表明，该模型在处理噪声图像和模糊图像时都能提供更有效的分割结果。
The performance of image segmentation highly relies on the original inputting image. When the image is contaminated by some noises or blurs, we can not obtain the efficient segmentation result by using direct segmentation methods. In order to efficiently segment the contaminated image, this paper proposes a two step method based on the hybrid total variation model with a box constraint and the K-means clustering method. In the first step, the hybrid model is based on the weighted convex combination between the total variation functional and the high-order total variation as the regularization term to obtain the original clustering data. In order to deal with non-smooth regularization term, we solve this model by employing the alternating split Bregman method. Then, in the second step, the segmentation can be obtained by thresholding this clustering data into different phases, where the thresholds can be given by using the K-means clustering method. Numerical comparisons show that our proposed model can provide more efficient segmentation results dealing with the noise image and blurring image.