基于多方法融合的非监督彩色图像分割

针对传统K-means聚类彩色图像分割方法需要人为设定初始分割类别数目、易受噪声干扰等缺陷，提出一种多方法融合非监督彩色图像分割算法。该算法对原始图像进行光谱信息增强处理以提高图像信息提取效率，对K-means聚类引入戴维森堡丁指数(Davies-Bouldin index, DBI)自动化确定最佳分割类别数目，通过图像聚类分析并进行像素标签标记，并结合高斯马尔科夫随机场(Gauss-Markov random field, GMRF)理论对标记图像进行分割，最后使用形态学算子进行后处理完成分割操作。试验结果表明。本研究方法具有一定的鲁棒性，且分割效果更接近真实性。通过对分割结果进行量化评价，进一步说明本研究方法在分割精度和准确性方面更具优势。

An unsupervised color image segmentation method based on fusion of multiple methods

An unsupervised color image segmentation method based on fusion of multiple methods was proposed, which considered the defects of traditional K-means clustering color image segmentation method, such as the need to set the number of initial segmentation categories artificially and the vulnerability to noise interference, etc. First of all, the original image was processed by spectral information enhancement to improving the efficiency of image information extraction. Next, the number of K-means clustering segmentation categories was determined automatically by using Davies-Bouldin Index, and the clustering analysis was carried out for images and each pixel in an image was labeled. Then, the labeled image was segmented by combining the Gauss-Markov random field theory. Finally, the image after-processing was made based on the morphological operators. The segmentation experiments were carried out by using different methods, the results showed that the segmentation effect of the proposed method was closer to the origin image, and the proposed method had good robustness. And the results of quantitative evaluation of segmentation showed that this method had more advantages in segmentation precision and accuracy.