基于分块权值的语义图像检索  

Semantic Image Retrieval Based on Sub-block Weight

作　　者：夏利民[1] 朱城[1] 张海燕[1] 彭东亮[1] 

机构地区：[1]中南大学信息科学与工程学院,长沙410075

出　　处：《计算机科学》2013年第9期266-269,共4页Computer Science

基　　金：国家自然科学基金(50808025);国家教育部博士点基金(20090162110057)资助

摘　　要：图像低层视觉特征和高层语义间的"语义鸿沟"是图像检索的关键问题。为了进一步提高基于语义的图像检索系统工作效率,以分块权值和视觉词库为基础,结合图像低层特征和高层语义的相关性,提出了一种基于分块权值的语义图像模型,该模型用来反映图像的视觉特性,对图像的高层语义进行有效检测,从而提高语义图像的检索效率。实验结果表明,该方法提高了语义图像检索系统的查全率和查准率。The semantic gap between low-level visual feature and high-level semantic has become a primary problem. For improving the efficiency of semantic-based image retrieval system, this paper based on chunked weight and a visual vocabulary proposed a semantic image model which utilizes the correlation of low-level feature and high-level semantic. The model is used to interpret the image visual characteristic and detecte high-level semantic, which improves the effi-ciency of the semantic image retrieval. The experimental results show that this method improves the precision and recall of semantic image retrieval system.

关 键 词：词袋 图像检索 子块 语义 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]