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A novel approach towards large scale cross-media retrieval

期刊名称：JCST
时间：2012.12.1
页码：1140-1149
分类：TP391[自动化与计算机技术—计算机应用技术;自动化与计算机技术—计算机科学与技术] G354.4[文化科学—情报学]
作者机构：[1]College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
相关基金：This work was supported by the National Natural Science Foundation of China under Grant Nos. 61025007, 60933001, 61100024, the National Basic Research 973 Program of China under Grant No. 2011CB302200-G, the National High Technology Research and Development 863 Program of China under Grant No. 2012AA011004, and the Fundamental Research Funds for the Central Universities of China under Grant No. Nl10404011.
相关项目：条件概率图数据管理关键技术研究

关键词：多媒体技术, 数据库检索, 语义空间, 多模态, 相关构造, 数据分配, 互联网, 异质性, cross-media retrieval, multi-modality, semantic correlation, indexing structure

中文摘要：

与因特网和多媒体技术的快速的开发，跨媒介的检索被担心由提交询问与多形式检索所有相关媒介对象媒介反对。不幸地，复杂性和多形式的异质为跨媒介的检索提出了下列二主要挑战：1 ) 怎么构造一统一并且为媒介的紧缩的模型与多形式反对， 2 ) 怎么为大规模改进检索的表演跨媒介的数据库。在这份报纸，我们建议是的一个新奇方法奉献给解决这些问题完成有效、精确的跨媒介的检索。第一，一种多形式语义关系图(MSRG ) 与多形式在媒介目标之中用语义关联被构造。第二，在 MSRG 的所有媒介目标被印射到一个同形的语义空格上。进一步，一棵有效索引 MK 树基于异构的数据分发被建议在语义空格以内管理媒介对象并且改进跨媒介的检索的性能。真实大规模的广泛的实验跨媒介的数据集显示我们的建议戏剧性地改进跨媒介的检索的精确性和效率，显著地超过存在方法。

英文摘要：

With the rapid development of Internet and multimedia technology, cross-media retrieval is concerned to retrieve all the related media objects with multi-modality by submitting a query media object. Unfortunately, the complexity and the heterogeneity of multi-modality have posed the following two major challenges for cross-media retrieval： 1） how to construct, a unified and compact model for media objects with multi-modality, 2） how to improve the performance of retrieval for large scale cross-media database. In this paper, we propose a novel method which is dedicate to solving these issues to achieve effective and accurate cross-media retrieval. Firstly, a multi-modality semantic relationship graph （MSRG） is constructed using the semantic correlation amongst the media objects with multi-modality. Secondly, all the media objects in MSRG are mapped onto an isomorphic semantic space. Further, an efficient indexing MK-tree based on heterogeneous data distribution is proposed to manage the media objects within the semantic space and improve the performance of cross-media retrieval. Extensive experiments on real large scale cross-media datasets indicate that our proposal dramatically improves the accuracy and efficiency of cross-media retrieval, outperforming the existing methods significantly.

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