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

• URL: http://arxiv.org/abs/2006.11510v2
• Date: Wed, 1 Jul 2020 01:43:13 GMT
• Title: G-image Segmentation: Similarity-preserving Fuzzy C-Means with Spatial Information Constraint in Wavelet Space
• Authors: Cong Wang and Witold Pedrycz and ZhiWu Li and MengChu Zhou and Shuzhi Sam Ge
• Abstract summary: This work elaborates a similarity-preserving Fuzzy C-Means (FCM) algorithm for G-image segmentation. 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.
• Score: 148.0882928072907
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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