基于内容的图像检索的相似度测量方法  

Effective Similarity Measure Method for Content-Based Image Retrieval

作　　者：王春静[1] 许圣梅[1] 

机构地区：[1]山东师范大学信息科学与工程学院,济南250014

出　　处：《数据采集与处理》2017年第1期104-110,共7页Journal of Data Acquisition and Processing

基　　金：国家自然科学基金(61170145;61373081)资助项目

摘　　要：图像特征匹配是基于内容的图像检索(Content-based image retrieval,CBIR)实现的一个关键环节,而图像特征的匹配主要依赖于图像特征之间的相似度测量。为了提高CBIR的检索性能,本文提出了一种有效的相似度测量方法——基于图像k近邻的相似度测量(Similarity measure based on k-nearest neighbors of images,SBkNN)方法。在该方法中,查询图像与被检索图像的相似度通过计算这两幅图像属于同一语义(无论是哪种语义)种类的联合概率来衡量,而此概率可分别通过分析这两幅图像与各自近邻图像的距离得到。最后利用Corel5k数据集对本文所提出的SBkNN方法和传统的相似度测量方法进行了对比。实验结果表明,在CBIR中使用本文提出的SBkNN方法,有效地提高了CBIR的检索性能。Image feature matching is a key link for the implementation of content-based image retrieval（CBIR）,which mainly relies on the similarity measure between the features of two images.To improve the retrieval performance of CBIR,this paper proposes an effective similarity measure method—similarity measure based on k-nearest neighbors of images（SBkNN）.In the proposed SBkNN method,the similarity between query image and retrieved image is obtained by calculating the probability for the two images belonging to the same semantic category（no matter what kind of semantic category）,and the probability can be obtained by analyzing the distance between the two images and their k-nearest neighbors,respectively.Finally,the comparison between the proposed SBkNN method and traditional similarity measure is implemented on Corel5 Kdataset.Experimental results show that the proposed SBkNN method significantly improves the retrieval performance of CBIR.

关 键 词：基于内容的图像检索 K近邻 相似度 召回率 查准率 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]