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首页> 外文期刊>Geoscience and Remote Sensing Letters, IEEE >Image Fuzzy Clustering Based on the Region-Level Markov Random Field Model
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Image Fuzzy Clustering Based on the Region-Level Markov Random Field Model

机译：基于区域级马尔可夫随机场模型的图像模糊聚类

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摘要

The Markov random field (MRF) model serves as one of the most powerful tools to improve the robustness of fuzzy c-means (FCM) clustering. However, the use of a pixel-level MRF makes the clustering deficient to deal with images with macro texture patterns. In order to overcome such a problem, this letter presents a novel method that segments images by combining FCM with the region-level MRF (RMRF) model. In this method, a fuzzy novel energy function is established for the RMRF model and utilized in the process of fuzzy clustering, which plays an important role in describing large-range variations of macro textures. Considering the complexity of image textures, a region-level mean template is also established to enhance the relationships between neighboring regions in terms of spectral and structural information. Experiments are conducted using high-resolution remote sensing images, which demonstrate that the proposed method can improve the segmentation accuracy compared with four state-of-the-art competitors.
机译：马尔可夫随机场（MRF）模型是提高模糊c均值（FCM）聚类的鲁棒性的最强大工具之一。但是，使用像素级MRF会使聚类不足以处理具有宏纹理图案的图像。为了克服这个问题，这封信提出了一种新颖的方法，该方法通过将FCM与区域级MRF（RMRF）模型相结合来分割图像。该方法为RMRF模型建立了一个模糊的新能量函数，并在模糊聚类的过程中加以利用，在描述宏观纹理的大范围变化中起着重要的作用。考虑到图像纹理的复杂性，还建立了区域级均值模板以增强光谱和结构信息方面的相邻区域之间的关系。使用高分辨率遥感影像进行的实验表明，与四个最先进的竞争对手相比，该方法可以提高分割精度。

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