A spatial constrained K-means approach to image segmentation
A spatial constrained K-means approach to image segmentation
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DOI：
10.1109/icics.2003.1292554
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发表时间：
2003-12
期刊：
影响因子：
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通讯作者：
M. Luo;Yufei Ma;HongJiang Zhang
中科院分区：
文献类型：
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作者：
M. Luo;Yufei Ma;HongJiang Zhang
在图像处理相关应用中，通用彩色图像分割是一个具有挑战性的重要问题。然而，很少有系统能够成功地为大量不同的映像处理这个问题。在本文中，我们正在寻求一种实用且通用的图像分割解决方案。作为一种快速分割过程，首先在特征空间中采用基于k均值的聚类。然后，在图像平面上，将空间约束引入到每一层的分层K-means聚类中。这两个过程交替地、迭代地进行。同时，提出了一种有效的区域合并方法来处理过分割问题。大量实验表明，该方法快速、通用，具有较好的应用价值。
General purposed color image segmentation is a challenging and important issue in image processing related applications. However, few systems successfully handle this issue for a broad diversity of images. In this paper, we are seeking a practical and generic solution to image segmentation. As a fast segmentation process, K-means based clustering is employed in feature space first. Then, in image plane, the spatial constraints are adopted into the hierarchical K-means clusters on each level. The two processes are carried out alternatively and iteratively. Also, an effective region merging method is proposed to handle the over segmentation. Extensive experiments show the proposed approach is fast and generic, thus practical in applications.