【摘要】 近年来,多模态数据挖掘技术备受关注,如何高效地挖掘大量多模态数据成为一个研究热点。其中,基于张量表示的多模态数据挖掘,即多模态张量数据挖掘,是一个重要的研究问题。综述了多模态张量数据挖掘算法进展及其在计算机视觉中的应用。首先根据算法的样本标记、任务和核心技术的不同,对这些方法进行分类,并给出了相应的介绍和分析。其次,讨论了一些多模态张量数据挖掘算法在计算机视觉问题中的典型应用。最后,就多模态张量挖掘在计算机视觉领域的研究现状与研究前景进行了简要的分析。更多还原

【Abstract】 Multi-modal data mining technologies have attracted many research interests in recent years.Mining large amount of multi-modal data efficiently becomes a hot spot problem.Among these multi-modal mining technologies,multi-modal data mining for tensor representation,which is also called as multi-modal tensor data mining,is one of the most significant research issues.We reviewed the state-of-the-art algorithms of the multi-modal tensor data mining and their applications in computer vision.Firstly,multi-modal tensor data mining algorithms were categorized into different classes according to the different label information,task and core technology.In addition,some analyses about these algorithms were given.Secondly,some typical multi-modal tensor mining algorithms in computer vision application were illustrated.Finally,we presented our own analyses on research status of multi-modal tensor mining algorithms,and explored some potential future issues of multi-modal tensor mining in computer vision application.更多还原