基于Hadamard变换的高维图像检索方法

传统索引方法对高维数据进行近邻搜索时会面临维数灾难问题,向量近似方法是一种有效的高维检索方法。提出一种 Hadamard 变换域上的向量近似方法,在变换域能量最大的分量上建立顺序索引,然后建立近似向量文件。同时提出低維过滤算法,可以在近邻搜索过程中高效排除不匹配近似向量,减少 I/O 访问时间,提高查询效率。在大型高维图像特征库上的实验表明,该方法性能优于小波变换域的向量近似方法。

A New Indexing Method for High-Dimensional Image Databases Using Hadamard Transform

Traditional indexing methods face the difficulty of'curse of dimensionality'at high dimensionality.The vec- tor approximation file(VA-File)approach based on wavelet transform is a very efficient high-dimensional indexing meth- od.In this paper,a new VA-File approach in the Hadamard transform domain is introduced.This approach combines Hadamard transform and principle component filtering algorithm,which can reduce the searching complexity and I/O cost dramatically on large image databases.Experiment results show that the new method is more efficient than VA- File based on wavelet transform.