基于相关反馈的高维图像检索方法

An efficient high-dimensional image indexing method for relevance feedback

传统索引方法在高维情况下会面临维数灾难问题,基于向量近似的索引方法是有效的高维检索方法.对向量近似方法中k近邻搜索算法加以改进,应用到基于相关反馈的交互式图像检索系统中.根据反馈过程前后的距离变化特性,在进行k近邻搜索过程中,将上轮次的查询结果和用户反馈信息用作过滤信息,可减少特征向量的访问数量.在大容量真实图像数据库上的实验表明,将新算法应用于相关反馈过程的图像检索中,可提高k近邻搜索速度.

Many traditional indexing methods perform poorly in the high-dimensional vector space. The Vector Approximation File approach overcomes some of the difficulties of curse of dimensionality. A new k-nearest neighbor search algorithm based on VA-File for relevance feedback image retrieval is introduced in this paper. Based on the feedback, the correlations of the underlying similarity metric between two search result and feedbacks and used to filter consecutive searches is exploited, and then the the approximate vectors in the next search round. Experiments on the large real-world dataset show a remarkable reduction of vectors accessed and an improvement on the indexing performance compared with the existing search algorithm.