Image Texture Segmentation Based on Cloud Fuzzy Theory
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摘要: 为了处理图像纹理的模糊性和随机性,基于云模糊理论提出了纹理特征矢量云模型,并成功地应用于纹理图像分割.该方法在对纹理统计描述符模糊化处理后,逆向生成纹理特征矢量云.矢量云模型的数字特征能够很好地表达纹理的模糊性和随机性,据此通过云距离计算及纹理特征矢量云生长,完成对图像纹理的分割.实验结果表明,该方法较经典的ISODATA算法和K-means簇算法的分割精度高,并且迭代收敛速度快.Abstract: To deal with the fuzziness and randomness of image textures,a vector cloud model for texture features was proposed on the basis of the cloud fuzzy theory and was successfully used to the segmentation of textural images.With this method,texture feature vector cloud is generated backward after fuzzily processing the statistical descriptor of textures.The fuzziness and randomness of textures can be represented perfectly by the digital characteristics of the vector cloud model,and as a result,image segmentation can be accomplished by cloud distance calculating and texture feature vector cloud growing.The research result shows that the segmentation accuracy is higher and the convergence speed is quicker using the proposed model than using the iterative self-organizing data analysis techniques algorithm(SODATA) and the K-means cluster algorithm.
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