A new fuzzy segmentation approach based on S-FCM type 2 using LBP-GCO features

Authors:
Lotfi Tlig;Mounir Sayadi;Farhat Fnaiech
Affiliations:
SICISI Unit, ESSTT, 5 Av. Taha Hussein, 1008 Tunis, Tunisia;SICISI Unit, ESSTT, 5 Av. Taha Hussein, 1008 Tunis, Tunisia;SICISI Unit, ESSTT, 5 Av. Taha Hussein, 1008 Tunis, Tunisia
Venue:
Image Communication
Year:
2012

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Abstract

Gabor filtering is a widely applied approach for texture analysis. This technique shows a strong dependence on certain number of parameters. Unfortunately, each variation of values of any parameter may affect the texture characterization performance. Moreover, Gabor filters are unable to extract micro-texture features which also have a negative effect on the clustering task. This paper, deals with a new descriptor which avoids the drawbacks mentioned above. The novel texture descriptor combines grating cell operator outputs derived from a designed Gabor filters bank, and local binary pattern features. For the clustering task, an extended version of fuzzy type 2 clustering algorithm is also proposed. The effectiveness of the proposed segmentation approach on a variety of synthetic and textured images is highlighted. Several experimental results on a set of textured images show the superiority of the proposed approach in terms of segmentation accuracy with respect to quantitative and qualitative comparisons.