G-image Segmentation: Similarity-preserving Fuzzy C-Means with Spatial Information Constraint in Wavelet Space

TL;DR

This paper introduces a novel similarity-preserving Fuzzy C-Means algorithm for G-image segmentation that leverages wavelet space and spatial information to enhance robustness and reduce computational complexity.

Contribution

It develops a new FCM algorithm incorporating spatial constraints and wavelet space, improving robustness and efficiency in G-image segmentation.

Findings

Achieves higher robustness than state-of-the-art FCM algorithms.

Requires less computation compared to existing methods.

Demonstrates effectiveness on synthetic and real-world G-images.

Abstract

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.