计算机科学 ›› 2019, Vol. 46 ›› Issue (1): 73-77.doi: 10.11896/j.issn.1002-137X.2019.01.011
李晓雨1, 聂秀山1, 崔超然1, 蹇木伟1, 尹义龙2
LI Xiao-yu1, NIE Xiu-shan1, CUI Chao-ran1, JIAN Mu-wei1, YIN Yi-long2
摘要: 近年来,随着互联网的发展和智能设备的普及,网络上存储的图片数量呈现爆发式增长,同时,不同类型的社交网络、媒体的用户数量也连续增长。在这种情况下,网络上的多媒体数据类型也发生了变革,在包含其本身携带的视觉信息的同时,也包含用户为其设定的标签信息、文本信息。在这种多模态信息杂糅的环境下,如何向用户提供快速准确的图像检索结果,是多媒体检索领域的一个新挑战。文中提出了一种基于迁移学习的图像检索算法,在对图像的视觉信息进行学习的同时,也对图像的文本信息进行学习,并将学习到的结果迁移到视觉信息领域,进行跨模态信息融合,进而产生包含跨模态信息的图像特征。经实验证明,所提算法能够实现更优的图像检索结果。
中图分类号:
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