Texture Segmentation Based on Dual Tree Complex Wavelet Transform and Support Vector Machine

This paper presents a new approach for segmentation of the textured images that exploits properties of the dual-tree complex wavelet transform, shift invariance and six directional sub-bands at each scale, and uses a feature vector comprising of mean and standard deviation of the six directional sub-bands over a sliding window. The classification of each sliding window using Support Vector Machine (SVM) leads to a segmented image. Through experiments on a variety of synthetic images of texture data sets, we show that our algorithm yields significant performance improvements for texture segmentation, as compared with other state-of-the-art methods of feature extraction.