Unsupervised perceptual segmentation of natural color images using fuzzy-based hierarchical algorithm

Authors:
Junji Maeda;Akimitsu Kawano;Sato Saga;Yukinori Suzuki
Affiliations:
Muroran Institute of Technology, Muroran, Japan;Muroran Institute of Technology, Muroran, Japan;Muroran Institute of Technology, Muroran, Japan;Muroran Institute of Technology, Muroran, Japan
Venue:
SCIA'07 Proceedings of the 15th Scandinavian conference on Image analysis
Year:
2007

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Abstract

This paper proposes unsupervised perceptual segmentation of natural color images using a fuzzy-based hierarchical algorithm. L*a*b* color space is used to represent color features and statistical geometrical features are adopted as texture features. A fuzzy-based homogeneity measure makes a fusion of color features and texture features. Proposed hierarchical segmentation method is performed in four stages: simple splitting, local merging, global merging and boundary refinement. Experiments on segmentation of natural color images are presented to verify the effectiveness of the proposed method in obtaining perceptual segmentation.