论文标题
g图像细分：相似性保护模糊c均值与小波空间中的空间信息约束
G-image Segmentation: Similarity-preserving Fuzzy C-Means with Spatial Information Constraint in Wavelet Space
论文作者
论文摘要
g图像是指在不规则图域上定义的图像数据。这项工作详细阐述了相似性的模糊C均值（FCM）算法，用于G-Image分割，并旨在开发用于分割G-Images的技术和工具。为了保留任意图像像素及其邻居之间的成员相似性，引入了kullback-leibler差异术语作为FCM的一部分。结果，通过考虑图像像素的稳健性增强的空间信息来开发相似性的FCM。由于小波空间的出色特征，在该空间中进行了提出的FCM，而不是在常规FCM中使用的欧几里得，以确保其高鲁棒性。关于合成和现实世界的G-Images的实验表明，与最先进的FCM算法相比，它确实可以实现更高的鲁棒性和性能。此外，它比大多数人所需的计算更少。
G-images refer to image data defined on irregular graph domains. This work elaborates a similarity-preserving Fuzzy C-Means (FCM) algorithm for G-image segmentation and aims to develop techniques and tools for segmenting G-images. To preserve the membership similarity between an arbitrary image pixel and its neighbors, a Kullback-Leibler divergence term on membership partition is introduced as a part of FCM. As a result, similarity-preserving FCM is developed by considering spatial information of image pixels for its robustness enhancement. Due to superior characteristics of a wavelet space, the proposed FCM is performed in this space rather than Euclidean one used in conventional FCM to secure its high robustness. Experiments on synthetic and real-world G-images demonstrate that it indeed achieves higher robustness and performance than the state-of-the-art FCM algorithms. Moreover, it requires less computation than most of them.
