CASIA OpenIR  > 模式识别国家重点实验室  > 多媒体计算与图形学
Learning Feature Hierarchies: A Layer-Wise Tag-Embedded Approach
Yuan, Zhaoquan1; Xu, Changsheng1; Sang, Jitao1; Yan, Shuicheng2; Hossain, M. Shamim3
Source PublicationIEEE TRANSACTIONS ON MULTIMEDIA
2015-06-01
Volume17Issue:6Pages:816-827
SubtypeArticle
AbstractFeature representation learning is an important and fundamental task in multimedia and pattern recognition research. In this paper, we propose a novel framework to explore the hierarchical structure inside the images from the perspective of feature representation learning, which is applied to hierarchical image annotation. Different from the current trend in multimedia analysis of using pre-defined features or focusing on the end-task "flat" representation, we propose a novel layer-wise tag-embedded deep learning (LTDL) model to learn hierarchical features which correspond to hierarchical semantic structures in the tag hierarchy. Unlike most existing deep learning models, LTDL utilizes both the visual content of the image and the hierarchical information of associated social tags. In the training stage, the two kinds of information are fused in a bottom-up way. Supervised training and multi-modal fusion alternate in a layer-wise way to learn feature hierarchies. To validate the effectiveness of LTDL, we conduct extensive experiments for hierarchical image annotation on a large-scale public dataset. Experimental results show that the proposed LTDL can learn representative features with improved performances.
KeywordAuto-encoder Deep Learning Hierarchical Feature Learning Social Tags
WOS HeadingsScience & Technology ; Technology
WOS KeywordCLASSIFICATION ; MULTIMEDIA ; MODELS ; REPRESENTATION ; RECOGNITION ; DICTIONARY ; MULTIPLE ; DATABASE
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Telecommunications
WOS SubjectComputer Science, Information Systems ; Computer Science, Software Engineering ; Telecommunications
WOS IDWOS:000354527500005
Citation statistics
Cited Times:4[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/8079
Collection模式识别国家重点实验室_多媒体计算与图形学
Affiliation1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
2.Natl Univ Singapore, Dept Elect & Comp Engn, Singapore 119077, Singapore
3.King Saud Univ, SWE Dept, Coll Comp & Informat Sci, Riyadh 11543, Saudi Arabia
Recommended Citation
GB/T 7714
Yuan, Zhaoquan,Xu, Changsheng,Sang, Jitao,et al. Learning Feature Hierarchies: A Layer-Wise Tag-Embedded Approach[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2015,17(6):816-827.
APA Yuan, Zhaoquan,Xu, Changsheng,Sang, Jitao,Yan, Shuicheng,&Hossain, M. Shamim.(2015).Learning Feature Hierarchies: A Layer-Wise Tag-Embedded Approach.IEEE TRANSACTIONS ON MULTIMEDIA,17(6),816-827.
MLA Yuan, Zhaoquan,et al."Learning Feature Hierarchies: A Layer-Wise Tag-Embedded Approach".IEEE TRANSACTIONS ON MULTIMEDIA 17.6(2015):816-827.
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