%0 Journal Article
%T Fast Image Search Using Vector Quantization<br>基于矢量量化的快速图像检索
%A YE Hang-Jun
%A XU Guang-You
%A <br>叶航军
%A 徐光祐
%J 软件学报
%D 2004
%I 
%X Traditional indexing methods face the difficulty of‘curse of dimensionality'at high dimensionality.Accurate estimate of data distribution and efficient partition of data space are the key problems in high-dimensional indexing schemes. In this paper, a novel indexing method using vector quantization is proposed. It assumes a Gaussian mixture distribution which fits real-world image data reasonably well. After estimating this distribution through EM(expectation-maximization) method, this approach trains the optimized vector quantizers to partition the data space, which will gain from the dependency of dimensions and achieve more accurate vector approximation and less quantization distortion. Experiments on a large real-world dataset show a remarkable reduction of I/O overhead of the vector accesses which dominate the query time in the exact NN (nearest neighbor) searches. They also show an improvement on the indexing performance compared with the existing indexing schemes.
%K CBIR (content-based image retrieval)
%K k-NN (nearest neighbor) search
%K high-dimensional indexing
%K curse of dimensionality
%K VQ (vector quantization)
%K EM (expectation-maximization)<br>基于内容的图像检索
%K k-近邻搜索
%K 高维索引
%K 维数灾难
%K 矢量量化
%K 期望最大化
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=7735F413D429542E610B3D6AC0D5EC59&aid=5D24C30462368056&yid=D0E58B75BFD8E51C&vid=23CCDDCD68FFCC2F&iid=94C357A881DFC066&sid=AB720B703F452703&eid=4D0B71A09FA5A2A5&journal_id=1000-9825&journal_name=软件学报&referenced_num=7&reference_num=19