  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.
