CASIA OpenIR  > 模式识别国家重点实验室  > 多媒体计算与图形学
Deep Relative Attributes
Yang, Xiaoshan1; Zhang, Tianzhu1; Xu, Changsheng1; Yan, Shuicheng2; Hossain, M. Shamim3; Ghoneim, Ahmed3,4
Source PublicationIEEE TRANSACTIONS ON MULTIMEDIA
2016-09-01
Volume18Issue:9Pages:1832-1842
SubtypeArticle
AbstractRelative attribute (RA) learning aims to learn the ranking function describing the relative strength of the attribute. Most of current learning approaches learn a linear ranking function for each attribute by use of the hand-crafted visual features. Different from the existing study, in this paper, we propose a novel deep relative attributes (DRA) algorithm to learn visual features and the effective nonlinear ranking function to describe the RA of image pairs in a unified framework. Here, visual features and the ranking function are learned jointly, and they can benefit each other. The proposed DRA model is comprised of five convolutional neural layers, five fully connected layers, and a relative loss function which contains the contrastive constraint and the similar constraint corresponding to the ordered image pairs and the unordered image pairs, respectively. To train the DRA model effectively, we make use of the transferred knowledge from the large scale visual recognition on ImageNet [1] to the RA learning task. We evaluate the proposed DRA model on three widely used datasets. Extensive experimental results demonstrate that the proposed DRA model consistently and significantly outperforms the state-of-the-art RA learning methods. On the public OSR, PubFig, and Shoes datasets, compared with the previous RA learning results [2], the average ranking accuracies have been significantly improved by about 8%, 9%, and 14%, respectively.
KeywordDeep Learning Relative Attributes (Ra)
WOS HeadingsScience & Technology ; Technology
DOI10.1109/TMM.2016.2582379
WOS KeywordCOMMUNITY-CONTRIBUTED PHOTOS ; OBJECT CLASSES ; RETRIEVAL
Indexed BySCI
Language英语
Funding OrganizationDeanship of Scientific Research at King Saud University, Riyadh, Saudi Arabia(RGP-229)
WOS Research AreaComputer Science ; Telecommunications
WOS SubjectComputer Science, Information Systems ; Computer Science, Software Engineering ; Telecommunications
WOS IDWOS:000381437800013
Citation statistics
Cited Times:8[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12644
Collection模式识别国家重点实验室_多媒体计算与图形学
Affiliation1.National Lab of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
2.Natl Univ Singapore, Dept Elect & Comp Engn, Singapore 117576, Singapore
3.King Saud Univ, Coll Comp & Informat Sci, Dept Software Engn, Riyadh 11543, Saudi Arabia
4.Menoufia Univ, Coll Sci, Dept Comp Sci, Menoufia 32721, Egypt
Recommended Citation
GB/T 7714
Yang, Xiaoshan,Zhang, Tianzhu,Xu, Changsheng,et al. Deep Relative Attributes[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2016,18(9):1832-1842.
APA Yang, Xiaoshan,Zhang, Tianzhu,Xu, Changsheng,Yan, Shuicheng,Hossain, M. Shamim,&Ghoneim, Ahmed.(2016).Deep Relative Attributes.IEEE TRANSACTIONS ON MULTIMEDIA,18(9),1832-1842.
MLA Yang, Xiaoshan,et al."Deep Relative Attributes".IEEE TRANSACTIONS ON MULTIMEDIA 18.9(2016):1832-1842.
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