Image Segmentation Algorithm Based on Non-Local Information and Subspace for Fuzzy C-Ordered Mean Clustering

The FCOM algorithm does not consider spatial information of images when processing noisy images, which leads to suboptimal segmentation results. To effectively remove noise interference, the Non-Local Information and Subspace for Fuzzy C-Ordered Mean (SFCOM_NLS) algorithm is proposed. Firstly, the non-local spatial information of each pixel is extracted using the given similar neighborhood structure in the image. Secondly, the typicality of each pixel is calculated and sorted, and the typicality of pixels is updated in each iteration to improve the accuracy of pixel clustering and solve the misclassification problem caused by similar classes during the clustering process. Finally, the concept of subspace clustering is introduced to allocate appropriate weights to different dimensions of the image, thus improving the segmentation performance of color images. Experimental results on synthetic images with noise and color image segmentation show that the proposed algorithm outperforms other compared algorithms in terms of fuzzy segmentation coefficient, fuzzy partition entropy, segmentation accuracy, and normalized mutual information.