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

In this paper we present a novel segmentation approach that performs fuzzy clustering and feature extraction. The proposed method consists in forming a new descriptor combining a set of texture sub-features derived from the Grating Cell Operator (GCO) responses of an optimized Gabor filter bank, and...

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Bibliographic Details
Main Authors: Tlig, Lotfi, Sayadi, Mounir, Fnaeich, Farhat
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
Clustering algorithms
Feature extraction
Frequency modulation
Fuzzy clustering
Gabor filtering
Image segmentation
Local binary pattern
Partitioning algorithms
Pixel
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Summary:In this paper we present a novel segmentation approach that performs fuzzy clustering and feature extraction. The proposed method consists in forming a new descriptor combining a set of texture sub-features derived from the Grating Cell Operator (GCO) responses of an optimized Gabor filter bank, and Local Binary Pattern (LBP) outputs. The new feature vector offers two advantages. First, it only considers the optimized filters. Second, it aims to characterize 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 method is demonstrated by several experiments on natural textures.
ISSN:2154-5111
2154-512X
DOI:10.1109/IPTA.2010.5586746