Back to Search
Start Over
G-Image Segmentation: Similarity-Preserving Fuzzy C -Means With Spatial Information Constraint in Wavelet Space.
- Source :
- IEEE Transactions on Fuzzy Systems; Dec2021, Vol. 29 Issue 12, p3887-3898, 12p
- Publication Year :
- 2021
-
Abstract
- G-images refer to image data defined on irregular graph domains. This article elaborates on 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 partition matrix 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 the 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 segmentation algorithms. Moreover, it requires less computation than most of them. [ABSTRACT FROM AUTHOR]
- Subjects :
- ALGORITHMS
PARALLEL algorithms
IMAGE segmentation
PIXELS
WAVELET transforms
Subjects
Details
- Language :
- English
- ISSN :
- 10636706
- Volume :
- 29
- Issue :
- 12
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Fuzzy Systems
- Publication Type :
- Academic Journal
- Accession number :
- 153924720
- Full Text :
- https://doi.org/10.1109/TFUZZ.2020.3029285