Department of Information Management, Technological Institute of Kavala, 65404 Kavala, Greece.
IEEE Trans Image Process. 2010 May;19(5):1328-37. doi: 10.1109/TIP.2010.2040763. Epub 2010 Jan 19.
This paper presents a variation of fuzzy c-means (FCM) algorithm that provides image clustering. The proposed algorithm incorporates the local spatial information and gray level information in a novel fuzzy way. The new algorithm is called fuzzy local information C-Means (FLICM). FLICM can overcome the disadvantages of the known fuzzy c-means algorithms and at the same time enhances the clustering performance. The major characteristic of FLICM is the use of a fuzzy local (both spatial and gray level) similarity measure, aiming to guarantee noise insensitiveness and image detail preservation. Furthermore, the proposed algorithm is fully free of the empirically adjusted parameters (a, ¿(g), ¿(s), etc.) incorporated into all other fuzzy c-means algorithms proposed in the literature. Experiments performed on synthetic and real-world images show that FLICM algorithm is effective and efficient, providing robustness to noisy images.
本文提出了一种模糊 C 均值（FCM）算法的变体，用于提供图像聚类。所提出的算法以新颖的模糊方式结合了局部空间信息和灰度信息。新算法称为模糊局部信息 C 均值（FLICM）。FLICM 可以克服已知模糊 C 均值算法的缺点，同时提高聚类性能。FLICM 的主要特点是使用模糊局部（空间和灰度）相似度度量，旨在保证对噪声不敏感和图像细节的保留。此外，所提出的算法完全没有文献中提出的所有其他模糊 C 均值算法中包含的经验调整参数（a、¿(g)、¿(s) 等）。在合成和真实世界图像上进行的实验表明，FLICM 算法是有效和高效的，对噪声图像具有鲁棒性。
