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中文摘要：
V‐系统是一类正交多小波，它的正交性和多分辨特性使得在表达信号时可以用少量的基函数去描述信号的基本特征，并且通过增加所用基函数的数量得到信号由粗到细的多层次特征描述。文中将V‐系统的这个特性应用到形状相似检索中，对图像边界和图像区域分别进行特征表达，得到了形状检索的新算法———V算法。首先提取图像的边界点列，并将其在V‐系统下作正交分解，得到由V‐描述子构成的边界特征向量，同时对图像区域作V‐变换，得到图像的区域特征向量；然后融合边界特征和区域特征来进行形状之间的相似度量。通过在4个通用数据库中的形状检索实验结果，表明了该算法相对几种经典算法在检索性能上的优势。
英文摘要：
The V‐system is a special orthogonal multiwavelet .Due to the virtue of its orthogonality and multi‐resolution ,only a small number of basis functions are required to describe characteristics of a signal .And multi level features of the signal ,from any coarse scale to a finer scale ,can be obtained by increasing the number of basis functions .This paper applies this characteristic of the V‐system on shape similarity retrieval ,and a new shape retrieval algorithm ,V‐algorithm ,is achieved by combing its boundary features and region features . First , the boundary of the image is extracted and orthogonally decomposed into a V‐series ,thereby obtaining a boundary feature vector consisting of the V‐descriptors .Meanwhile , a region feature vector of the image is obtained by applying the V‐transform to the image region .Then similarity between shapes is measured using an integrated shape descriptor ,which combines the boundary features and region features . The results of experiments conducted on four benchmark databases show that the method in this work has advantage in retrieval efficiency in comparison with several classical algorithms .
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