典型文献
Unsupervised Cross-Media Hashing Learning via Knowledge Graph
文献摘要：
With the rapid growth of multimedia data,cross-media hashing has become an important tech-nology for fast cross-media retrieval.Because the manual annotations are difficult to obtain in real-world applica-tion,unsupervised cross-media hashing is studied to ad-dress the hashing learning without manual annotations.Existing unsupervised cross-media hashing methods gen-erally focus on calculating the similarities through the fea-tures of multimedia data,while the learned hashing code cannot reflect the semantic relationship among the multi-media data,which hinders the accuracy in the cross-me-dia retrieval.When humans try to understand multime-dia data,the knowledge of concept relations in our brain plays an important role in obtaining high-level semantic.Inspired by this,we propose a knowledge guided unsuper-vised cross-media hashing(KGUCH)approach,which ap-plies the knowledge graph to construct high-level semant-ic correlations for unsupervised cross-media hash learning.Our contributions in this paper can be summarized as fol-lows:1)The knowledge graph is introduced as auxiliary knowledge to construct the semantic graph for the con-cepts in each image and text instance,which can bridge the multimedia data with high-level semantic correlations to improve the accuracy of learned hash codes for cross-media retrieval.2)The proposed KGUCH approach con-structs correlation of the multimedia data from both the semantic and the feature aspects,which can exploit com-plementary information to promote the unsupervised cross-media hash learning.The experiments are conduc-ted on three widely-used datasets,which verify the effect-iveness of our proposed KGUCH approach.
文献关键词：
中图分类号：
作者姓名：
YE Zhaoda;HE Xiangteng;PENG Yuxin
作者机构：
Wangxuan Institute of Computer Technology,Peking University,Beijing 100091 China
文献出处：
引用格式：
[1]YE Zhaoda;HE Xiangteng;PENG Yuxin-.Unsupervised Cross-Media Hashing Learning via Knowledge Graph)[J].电子学报（英文）,2022(06):1081-1091
A类：
multime,unsuper,KGUCH,semant,iveness
B类：
Unsupervised,Cross,Media,Hashing,Learning,via,Knowledge,Graph,With,rapid,growth,multimedia,cross,hashing,become,important,tech,nology,fast,retrieval,Because,manual,annotations,are,difficult,real,world,applica,unsupervised,studied,ad,dress,learning,without,Existing,methods,gen,erally,focus,calculating,similarities,through,tures,while,learned,cannot,reflect,semantic,relationship,among,which,hinders,accuracy,When,humans,try,understand,knowledge,concept,our,brain,plays,role,obtaining,high,level,Inspired,by,this,we,guided,approach,plies,graph,construct,correlations,Our,contributions,paper,summarized,fol,lows,introduced,auxiliary,cepts,each,image,text,instance,bridge,improve,codes,proposed,structs,from,both,feature,aspects,exploit,plementary,information,promote,experiments,conduc,ted,three,widely,used,datasets,verify,effect
AB值：
0.454422
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