基于非抽样轮廓波变换和矩阵F-范数的旋转不变纹理图像检索方法

Rotation-invariant Texture Image Retrieval Based on Nonsubsampled Contourlet Transform and Matrix F-norm

针对纹理图像检索中常见的旋转问题,提出一种基于非抽样Contourlet变换(NSCT)和矩阵F-范数的旋转不变纹理图像检索算法。对图像进行NSCT变换,以NSCT域各个子带系数矩阵的F-范数构造特征向量,然后在相同尺度上,利用各个子带系数矩阵的均值和标准方差之和对特征向量由小到大排序,再利用不同尺度的特征向量相似度加权求和得到两幅图像的相似度。实验采用Brodatz库生成实验库,结果表明该方法能取得较好的效果。

This paper proposes a novel rotation-invariant texture image retrieval algorithm based on nonsubsampled contourlet transform （NSCT） and matrix F-norm against the common rotation problems for image retrieval.By calculating the F-norm of image subbands decomposed by NSCT,the texture feature elements are extracted.For each scale,the feature elements are re-ordered ascending by the sum of mean and standard deviation of each image subband,the similarity of two images is calculated by the feature vector similarity weighting of different scales.Experiments are conducted on the image set produced by Brodatz database,and the result demonstrates superiority of the proposed method.