Zernike矩是一种基于区域的形状描述符，它适合于描述具有复杂边界的目标。原始的Zernike矩描述符认为形状中任何位置的像素都具有相同的重要性，基于此，提出了一种改进的Zernike矩描述符。它首先采用预定义的两个半径值对原始形状进行分块，提取各分块的Zernike矩值作为图像的形状特征向量，然后采用相关反馈信息和各分块间的距离方差来动态调整各个分块的权值系数，根据欧式距离来计算图像间的相似度。改进的Zernike矩描述符可以根据人类视觉特征灵活确定形状各部分的重要性，而且相关反馈技术使得提取出来的图像更加接近用户的需求。实验结果表明，该方法能够有效地改善基于形状特征的检索效果。

Zernike moments are used as a shape descriptor for complex shapes that are difficult to be defined with a single contour such as trademarks. The Zernike moments of a given shape are calculated as correlation values of the shape with Zernike basis functions in that all the pixels of the shape regardless of their positions contribute with the same weight to the Zernike moments. The proposed modified Zernike Moment descriptor is obtained by the following two steps: firstly divide the original shape into three parts of inner, middle and outer regions with tow predetermined radius, then calculate the Zernike moment of each part reseparateively. The modified descriptor takes account of the partition radius of the shape according to human perception, meanwhile, using relevance feedback technology to fix the importance of the each part as mentioned above could improve the efficiency of retrieval process. Euclidean distance is used to compute the distance between two shapes. Experimentation under various test conditions shows the effectiveness of the proposed modified method.