G-image Segmentation: Similarity-preserving Fuzzy C-Means with Spatial Information Constraint in Wavelet Space
This work addresses segmentation challenges for G-images, which are common in domains like medical imaging or network analysis, but it appears incremental as it builds on existing FCM methods with specific enhancements.
The paper tackled the problem of segmenting G-images (images on irregular graph domains) by developing a similarity-preserving Fuzzy C-Means algorithm with spatial constraints in wavelet space, achieving higher robustness and performance than state-of-the-art FCM algorithms while requiring less computation.
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.