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中文摘要：
为了更准确地进行跨媒体检索，需要挖掘、学习不同类型多媒体对象之间的语义关联，为此提出一种基于综合推理模型的多媒体语义挖掘和跨媒体检索技术．首先根据多媒体对象的底层特征构造推理源，根据多媒体对象的共生关系构造影响源场来进行综合推理，并构造出多媒体语义空间；然后针对不同检索例子，根据伪相关反馈为每一个检索例子自适应地选择不同的检索方法进行跨媒体检索．为了处理检索例子不在训练集合内的情况，提出了两阶段学习方法完成检索；同时还提出了一种基于日志的长程反馈学习算法，以提高系统性能．实验结果证明，该技术能够准确地挖掘多媒体语义，多媒体文档检索和跨媒体检索效果准确且稳定．
英文摘要：
To gain better cross-media retrieval performance, it is crucial to mine the semantic correlations among the heterogeneous multimedia data. In this paper, we adopt the synthesis reasoning model as the underlying mechanism to mining the multimedia semantics for cross-media retrieval. We construct the synthesis reasoning sources according to the multimedia object low-level features and the reasoning source intensity field according to the multimedia co-existence information. A series of multimedia semantic spaces are built by spectral method after synthesis reasoning. The cross-media retrieval is performed on a per-query basis by which different retrieval methods are adopted for different queries. Both short term and long term relevance feedback are learned to introduce the new multimedia objects into the multimedia semantic spaces which were not in the training set, to refine the reasoning result. Experimental results show that the proposed methods can be used to accurately mine the multimedia semantics and the approach of cross-media retrieval is accurate and stable.
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