发掘相关反馈日志中关联信息的图像检索方法  

Enhancing Content-Based Image Retrieval by Exploiting Relevance Feedback Logs

作　　者：张亮[1] 施伯乐[1] 周向东[1] 刘莉[1] 张琪[1] 

机构地区：[1]复旦大学计算机与信息技术系,上海200433

出　　处：《软件学报》2004年第1期41-48,共8页Journal of Software

基　　金：国家自然科学基金~~

摘　　要：相关反馈日志蕴含着丰富的对象语义关联信息,但大多数基于内容的图像检索(CBIR)方法却缺乏对它们的重用.提出一种发掘反馈日志中图像关联信息的自动化图像检索方法,将反馈事例中图像的共生现象视为一定上下文中的图像分类.检索时,结合CBIR的检索结果和多种上下文中的图像分类实例,借鉴HITS算法的思想从中提炼图像的本质性关联,获得综合内容和语义的图像检索结果.对6万幅Corel图像数据库的实验表明,该方法可以显著改善查全率和查准率,且检索结果能够更好地满足用户的语义检索需求.Relevance feedback (RF) has been successfully used in content-based image retrieval (CBIR). However, most CBIR systems seldom reuse the latent semantic correlation among images revealed by RF log to guide retrieving across sessions. In this paper, concurrence of images in a RF record is regarded as a kind of semantic homogeneity in certain context and the image-retrieving problem is cast as an authority-image-finding task. Records in RF logs first extend the result from traditional CBIR systems. This produces a relevant graph of images related to the query with multiplex contexts. Then, a modified HITS algorithm is applied to it to distill consensus about semantic relevance. As a result, both visual content and semantic relevance can be maintained in image retrieval and the efficiency is much improved compared with traditional CBIR methods. Experimental results demonstrate its superiority in both objective criteria and semantic clustering capability against the Corel database with 60000 images.

关 键 词：图像检索 相关反馈日志 图像内容 图像语义 HITS算法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]