针对图像检索中常见的旋转问题，提出了一种基于非抽样Contourlet变换(NSCT)的旋转不变检索算法。以NSCT域各子带系数的均值和标准方差构成基本特征向量。在相同尺度上，利用各子带的均值和标准方差之和对特征分量由小到大排序，同时根据旋转不变性调整排序后特征向量对应的方向序列，构造方向序列权值、特征分量权值。用加权欧氏距离进行相似性度量以提高检索性能。采用Brodatz库生成实验库，实验结果表明本文方法取得了较好的检索效果。

This paper proposes a rotation-invariant algorithm based on NSCT against the common rotation problems for image retrieval. By calculating the mean and standard deviation 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, and the orientation sequences are adjusted by considering rotation-invariant to create orientation-weights and feature element-weights. Weighted euclidean distance was used to improve retrieval efficiency. Experiments were conducted on the image set produced by Brodatz database, and the results demonstrate superiority of the proposed method.