基于迁移学习的图像检索算法

Image Retrieval Algorithm Based on Transfer Learning

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。

In recent years,with the development of the Internet and the popularity of smart devices,the number of online store image is explosively growing.At the same time,the number of users who use different types of social networks and media continues to grow.In this case,the multimedia data type that the user uploaded to the network also has changed,the image uploaded by the user contains the visual information that is carried by the image itself,and also contains the label information and text information that the user sets for it.Therefore,how to provide fast and accurate image retrieval results to users is a new challenge in the field of multimedia retrieval.This paper proposed an image retrieval algorithm based on transfer learning.It learns the visual information and the text information at the same time,then migrates the results learnt to the visual information domain,and thus the feature contains cross modal information.Experimental results show that the proposed algorithm can achieve better image retrieval results.