现代电子技术Issue(10)：73-77,5.
基于Gabor特征的稀疏表示纹理分割研究
Gabor feature based sparse representation for texture segmentation
摘要
Abstract
The method of sparse representation for texture segmentation is to convert the image segmentation into the pixel classification. Generally,the method of sparse representation classification is based on image block feature,which is difficult to accurately character the image’s texture information. To solve the above⁃mentioned problems,Gabor feature based sparse repre⁃sentation for texture segmentation is proposed in this paper,because Gabor feature is robustness to image texture. Firstly,some pixels are randomly select from each texture as training samples to calculate their Gabor features with different scales and orien⁃tations,and take these Gabor features as initialization dictionary. The dictionary is updated by discriminative dictionary learning (D⁃KSVD)algorithm. Based on KSVD,the algorithm makes the dictionary more discriminative. Finally,each pixel of the under segment image is taken as the test samples to calculate their Gabor features. The OMP algorithm is utilized to calculate the sparse coefficients to obtain the final class labels. The result of experiment on the Brodatz texture database shows that the pro⁃posed method can effectively improve the texture segmentation accuracy of sparse representation algorithm.关键词
稀疏表示/字典学习/D-KSVD/GaborKey words
sparse representation/dictionary learning/D-KSVD/Gabor分类
信息技术与安全科学引用本文复制引用
蒋宏骏,纪则轩,孙权森..基于Gabor特征的稀疏表示纹理分割研究[J].现代电子技术,2015,(10):73-77,5.基金项目
国家自然科学基金 （）
