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首页> 外文会议>Computational Intelligence for Image Processing, 2009. CIIP '09 >A modified fuzzy c-means algorithm with adaptive spatial information for color image segmentation
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A modified fuzzy c-means algorithm with adaptive spatial information for color image segmentation

机译：一种改进的具有自适应空间信息的模糊c均值算法用于彩色图像分割

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Though FCM has long been widely used in image segmentation, it yet faces several challenges. Traditional FCM needs a laborious process to decide cluster center number by repetitive tests. Moreover, random initialization of cluster centers can let the algorithm easily fall onto local minimum, causing the segmentation results to be suboptimal. Traditional FCM is also sensitive to noise due to the reason that the pixel partitioning process goes completely in the feature space, ignoring some necessary spatial information. In this paper we introduce a modified FCM algorithm for color image segmentation. The proposed algorithm adopts an adaptive and robust initialization method which automatically decides initial cluster center values and center number according to the input image. In addition, by deciding the window size of pixel neighbor and the weights of neighbor memberships according to local color variance, the proposed approach adaptively incorporates spatial information to the clustering process and increases the algorithm robustness to noise pixels and drastic color variance. Experimental results have shown the superiority of modified FCM over traditional FCM algorithm.
机译：尽管FCM长期以来一直广泛用于图像分割，但仍面临一些挑战。传统的FCM需要费力的过程才能通过重复测试确定集群中心号码。此外，聚类中心的随机初始化可以使算法容易地落入局部最小值，从而导致分割结果不理想。由于像素分割过程完全在特征空间中进行，而忽略了一些必要的空间信息，因此传统的FCM对噪声也很敏感。在本文中，我们介绍了一种用于彩色图像分割的改进型FCM算法。所提出的算法采用自适应鲁棒的初始化方法，该方法根据输入图像自动确定初始聚类中心值和中心号。此外，通过根据局部颜色差异确定像素邻居的窗口大小和邻居成员的权重，该方法将空间信息自适应地纳入聚类过程，并提高了算法对噪声像素和剧烈颜色差异的鲁棒性。实验结果表明，改进型FCM优于传统FCM算法。

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