2006 | OriginalPaper | Buchkapitel
Texture Segmentation Using Kernel-based Techniques
verfasst von : Yu-Long Qiao, Zhe-Ming Lu, Sheng-He Sun
Erschienen in: Advances in Computer, Information, and Systems Sciences, and Engineering
Verlag: Springer Netherlands
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Texture segmentation is an important component in texture analysis. Gabor wavelet shows potential capacity in describing the texture. Kernel-based methods have demonstrated excellent performances in a variety of pattern recognition problems. This paper extracts texture features in the Gabor wavelet transform domain and segments the computed feature image with kernel-based techniques, Spectral Clustering Algorithm (SCA) and Support Vector Machine (SVM). Due to the severe computational complexity of kernel-based techniques, we split the main segmentation process into two steps. SCA is firstly applied to the sampled feature image. Then the clustering result serves as training samples and is used to train SVM. The initial segmentation result is obtained by labeling the feature image with the trained SVM. A median filter is employed to improve the segmentation result. Four mosaic texture images and a natural scene are used to test our new algorithm.