Title: A new descriptor for textured image segmentation based on fuzzy type-2 clustering approach

Authors: Lotfi Tlig; Mounir Sayadi; Farhat Fnaiech

Addresses: University of Tunis SIME Laboratory, ESSTT, 5 Av. Taha Hussein, 1008, Tunis, Tunisia ' University of Tunis SIME Laboratory, ESSTT, 5 Av. Taha Hussein, 1008, Tunis, Tunisia ' University of Tunis SIME Laboratory, ESSTT, 5 Av. Taha Hussein, 1008, Tunis, Tunisia

Abstract: In this paper we present a novel segmentation approach that performs fuzzy clustering and feature extraction. The proposed method consists to forming a new descriptor combining a set of texture sub-features derived from the Grating Cell Operator (GCO) responses of an optimised Gabor filter bank, and Local Binary Pattern (LBP) outputs. The new feature vector offers two advantages. First, it only considers the optimised filters. Second, it aims to characterise both micro and macro textures. In addition, an extended version of a type 2 fuzzy c-means clustering algorithm is proposed. The extension is based on the integration of spatial information in the membership function (MF). The performance of this technique is demonstrated by several experiments on natural textures.

Keywords: Gabor filtering; local binary patterns; fuzzy clustering; image segmentation; descriptors; textured images; feature extraction; fuzzy c-means; natural textures; image processing.

DOI: 10.1504/IJSISE.2014.065263

International Journal of Signal and Imaging Systems Engineering, 2014 Vol.7 No.3, pp.159 - 166

Received: 16 Jul 2012
Accepted: 30 Jul 2012
Published online: 21 Oct 2014 *

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