Robust joint learning of superpixel generation and superpixel-based image segmentation using fuzzy C-multiple-means clustering

In recent years, many superpixel-based image segmentation algorithms have been presented. However, most of these algorithms face issues of high model complexity and weak robustness. There are two main reasons for this. On the one hand, the traditional superpixel-based image segmentation algorithm constructs two different models to complete the tasks of superpixel generation and superpixel-based image segmentation, which significantly increase the complexity of the algorithm. On the other hand, the segmentation results are largely affected by the characteristics of the generated superpixels, and many superpixel generation algorithms are sensitive to noise, resulting in poor robustness. Therefore, this paper proposes a novel superpixel-based fuzzy C-multiple-means clustering algorithm, which generates superpixels and segments superpixel image in one step to reduce the complexity of the algorithm. Meanwhile, the proposed algorithm introduces local spatial information of pixel, enhancing the robustness of superpixel generation to noise. In addition, the generated superpixels have centroid shift property, further improving the algorithm's detail-preservation ability and robustness. Experimental results show that this algorithm outperforms many state-of-the-art superpixel-related and unrelated fuzzy clustering algorithms in noisy image segmentation, and consumes less time when providing similar segmentation results. Overall, the work in this paper will greatly promote the development of superpixel-based image segmentation theory.