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刘 爽 白 亮 于天元 贾玉华 (国防科学技术大学信息系统工程重点实验室 长沙410073)  摘 要 随着互联网技术的迅猛发展,网络信息的呈现形式不断从简单的文本扩展到图像、声音、视频等多媒 体 表 达 形式。在多媒体信息检索领域中,传统方法 往 往 在 同 一 个 特 征 空 间 中 表 示 所 有 的 媒 体 模 式,并 采 取 一 对 一 的 配 对 数 据,或者利用单向排序实例作为训练样本进行检索。在此背景下,考虑了学习双向排序实例,进而实现了 跨 媒 体 检 索 的方法。在 Wikipedia数据集上进行测试,实验结果表明,基于双向排序的跨媒体语义相似性度量方法具有更好的性 能。 关键词 跨媒体表示,双向学习排序,隐空间,相似性度量 中图法分类号 TP393 文献标识码 A  Cross-media Semantic Similarity Measurement Using Bi-directional Learning Ranking LIU Shuang BAI Liang YU Tian-yuan JIA Yu-hua (Key Laboratory of Information System Engineering,National University of Defense Technology,Changsha 410073,China)  Abstract With the rapid development of Internet technology,the presented forms of network information have extended from simple text to images,voice,video and other multimedia expression.In the field of multimedia information retrieval,the traditional methods often represent al of the media mode in the same feature space model.Existing methods take either one-to-one paired data or uni-directional ranking examples.In this paper,we considered learning bi-directional ranking examples in the cross-media retrieval.By analyzing the experimental results basing on the Wikipedia dataset,it is demonstrated better performance of the proposed method.

基于双向学习排序的跨媒体语义相似性度量方法 (Cross-media Semantic Similarity Measurement Using Bi-directional Learning Ranking).