PSO-AHC聚类算法在图像检索中的应用研究

传统的基于内容的图像检索技术使用的是顺序检索,对于海量、高维的图像数据来说,这种检索方法在效率上显然已经不能满足需要。为了进行必要的预图像数据库和索引,以提高检索效率变得更加重要。因此,本文引入高效的k-means聚类算法和凝练层次聚类进行图像数据库预处理,构建层次化索引结构,达到快速检索的目的。

the traditional content-based image retrieval technology USES sequential retrieval.For massive and high-dimensional image data,this retrieval method is obviously unable to meet the needs in efficiency.In order to carry out the necessary pre-image database and index,it becomes more important to improve the retrieval efficiency.Therefore,this paper introduces an efficient k-means clustering algorithm and condensed hierarchical clustering to preprocess the image database,build a hierarchical index structure,and achieve the purpose of fast retrieval.