Adaptive fuzzy weighted C-mean image segmentation algorithm combining a new distance metric and prior entropy

• Published in: Engineering applications of artificial intelligence 2024-05, Vol.131, p.107776, Article 107776
• Main Authors: Song, Sensen, Jia, Zhenhong, Shi, Fei, Wang, Junnan, Ni, Dongdong
• Format: Article
• Language: English
• Subjects: Distance metric; FCM; Fuzzy weight; Image segmentation; Initialization settings; Prior entropy
• ISSN: 0952-1976; 1873-6769
• DOI: 10.1016/j.engappai.2023.107776

The fuzzy C-mean clustering algorithm (FCM) is effective in image segmentation. However, its sensitivity to initialization settings and the limitation of the Euclidean distance metric in measuring similarity lead to unsatisfactory image segmentation. To overcome these shortcomings, we propose an adaptive fuzzy weighted C-mean image segmentation algorithm that combines a new distance metric and prior entropy. This algorithm significantly contributes in three aspects. First, we employ an improved sparse subspace clustering (SSC) algorithm based on the superpixel image for initialization settings. This step aims to obtain the initial number of clusters and the membership matrix, ensuring the algorithm’s convergence to the optimal solution. Second, to better capture differences between image regions, we introduce a novel distance metric that combines Euclidean and non-Euclidean distances. Simultaneously, the fuzzy weight is introduced to mitigate redundant feature interference, making the clustering result more reasonable. Finally, leveraging prior entropy – a maximum entropy under conditional constraints – we refine the algorithm’s adaptability to unknown features, thereby improving clustering accuracy. Comparative experiments with several state-of-the-art algorithms validate the effectiveness and robustness of our proposed algorithm. •A Gaussian-constrained SSC algorithm is proposed as the initialization method.•Using a new distance metric and fuzzy weight to overcome the shortcomings of the Euclidean distance.•Using prior entropy to process uncertainty can indeed improve the performance.•An adaptive fuzzy weighted C-mean structure is designed.•Experiments demonstrate the better performance of the proposed method.