A novel joint learning framework combining fuzzy C-multiple-means clustering and spectral clustering for superpixel-based image segmentation

Abstract

In recent years, image segmentation algorithms based on superpixels have been continuously developed. However, the superpixel algorithm consists of two independent stages: superpixel generation and superpixel segmentation. When the generation of superpixels is influenced by noise or complex backgrounds, the quality of the generated superpixel image can significantly decline, adversely affecting the subsequent segmentation results. Therefore, this paper proposes a robust multiple-means joint clustering algorithm based on superpixels, which integrates superpixel generation and superpixel image segmentation within a unified learning framework. This approach achieves multiple-means joint clustering by alternately optimizing and updating superpixel and sub-cluster centers. Compared with traditional superpixel segmentation algorithms, this method does not generate superpixels separately and demonstrates improved segmentation performance. Additionally, the algorithm incorporates spectral clustering to transform the superpixel image segmentation problem into a constrained Laplacian matrix rank optimization problem, ultimately achieving clustering based on bipartite graph connectivity, which further enhance the algorithm's robustness. Numerous experimental results indicate that the proposed algorithm yields superior segmentation outcomes compared with existing other superpixel segmentation algorithms and aligns more closely with real-world segmentation details.