三特征Contourlet纹理图像检索算法

Three Feature Contourlet Texture Image Retrieval Algorithm

  • 摘要: Contourlet纹理检索算法中,特征向量的构造大多采用子带系数的能量和标准偏差级联得到,这在一定程度上阻碍了细节纹理的准确表达.为了克服这个问题,构造了相对标准偏差这个特征作为纹理特性的描述算子,并将该特征与子带系数幅度均值和峰度级联来构造特征向量.采用这种新型的特征向量,在Contourlet与Contourlet-2.3两种轮廓波纹理算法框架下,以Brodatz纹理图像数据库作为测试对象,相对于目前普遍采用的特征向量,平均检索率提高了5%~7%,从而验证了该算法的有效性

     

    Abstract: Feature vector construction of contourlet texture image retrieval algorithm generally was generated by cascading energy and variance of each sub-band coefficients, hence preventing the exact expression of detailed texture structure. To alleviate the defect of this reason, relative variances was constructed as a new descriptor for texture characters, and further combine the descriptor with the magnitude of sub-band coefficients and kurtosis to construct new feature vector of each texture image. Using this kind of novel feature vector, in the framework of contourlet and contourlet-2.3 texture image retrieval systems, experiments on texture images from Brodatz showed that the new algorithm is superior to the commonly used feature vector, average retrieval rates can be improved about 5%~7%

     

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