.png){kind=icon}
.png){kind=icon}
Return
Broad learning surrogate-assisted multi-objective evolutionary rough fuzzy clustering algorithm for color image segmentation
.png){kind=icon}
PRE
AI
F
W
H
J
J
DOI：10.1007/s00500-025-11019-7.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Rough-fuzzy clustering has been successfully applied in image segmentation owing to its superior capability in handling data uncertainty and ambiguity. However, it still has several shortcomings, including manual determination of threshold parameters, reliance on a single clustering criterion function, sensitivity to initialized cluster centers, and a tendency to fall into local optimum. To address these issues, this paper introduces surrogate-assisted multi-objective optimization into rough fuzzy clustering and proposes an enhanced broad learning system (BLS) guided surrogate-assisted multi-objective evolutionary rough fuzzy clustering (EBLS-MERFC) algorithm. The algorithm employs a genetic algorithm to replace traditional traversal methods for selecting optimal points, constructing an enhanced BLS model with parameter adaptive determination strategy. It predicts fitness function values to substitute real function calculations, ensuring prediction performance while reducing computational costs. An adaptive threshold determination mechanism for rough-fuzzy clustering is established based on differences in fuzzy membership degrees, which effectively divides the lower approximation region and boundary region in rough-fuzzy clustering while minimizing manual intervention. Considering the proportion of cluster boundary regions, a rough-fuzzy intra-class compactness fitness function is constructed, which is synchronously optimized with the rough-fuzzy inter-class separability function to obtain segmentation results. Experimental results on Berkeley and Weizmann natural images demonstrate that compared with other multi-objective evolutionary clustering algorithms, the proposed algorithm exhibits superior segmentation accuracy and robustness.
Keywords:
Color image segmentation
Multi-objective optimization
Rough fuzzy clustering
Broad learning system
Surrogate-assisted evolutionary algorithm
Journal
IF:
2.5
Papers:
1.0W
Citations:
2.1W
Organization
Cited Papers