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首页> 外文会议>International symposium on multispectral image processing and pattern recognition >Adaptive region constrained FCM algorithm for image segmentation with hierarchical superpixels
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Adaptive region constrained FCM algorithm for image segmentation with hierarchical superpixels

机译：分层超像素图像分割的自适应区域约束FCM算法

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摘要

Spatially fuzzy c-means (FCM) clustering has been successfully applied in the field of image segmentation. However, duo to the existence of noise and intensity inhomogeneity in images, most of the spatial constraint model fail to resolve misclassification problem. To further improve the segmentation accuracy, a robust spatially constrained FCM-based image segmentation method with hierarchical region information is proposed in this paper. First, two-level superpixles of the input image are generated by two classical segmentation methods, and the first level superpixels instead of the pixels are as inpvit of FCM. Second, by considering the use of the spatial constraints with high-level superpixels, a novel membership function of the first-level superpixels is designed to overcome the impact of noise in the image and accelerate the convergence of clustering process. Through using superpixels instead of pixels and incorporating superpixel information into the spatial constraints, the proposed method can achieve highly consistent segmentation results. Experimental results on the Berkeley image database demonstrate the good performance of the proposed method.
机译：空间模糊c均值（FCM）聚类已成功应用于图像分割领域。然而，由于图像中存在噪声和强度不均匀性，大多数空间约束模型无法解决分类错误问题。为了进一步提高分割精度，提出了一种鲁棒的基于空间约束的基于FCM的具有分层区域信息的图像分割方法。首先，通过两种经典的分割方法生成输入图像的两级超像素，并且第一级超像素代替像素是FCM的像素。其次，通过考虑对高级超像素使用空间约束，设计了一种新的一级超像素隶属函数，以克服图像中噪声的影响并加速聚类过程的收敛。通过使用超像素代替像素并将超像素信息纳入空间约束，该方法可以实现高度一致的分割结果。在伯克利图像数据库上的实验结果证明了该方法的良好性能。

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