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Multimodal Web Aesthetics Assessment Based on Structural SVM and Multitask Fusion Learning
Wu, Ou1; Zuo, Haiqiang2; Hu, Weiming1; Li, Bing1; Ou Wu
Source PublicationIEEE TRANSACTIONS ON MULTIMEDIA
2016-06-01
Volume18Issue:6Pages:1062-1076
SubtypeArticle
AbstractThe overall visual attributes (e.g., aesthetics) of Web pages significantly influence user experience. A beautiful and well laid out Web page greatly facilitates user access and enhances the browsing experience. In this paper, a new method is proposed to learn an assessment model for the (visual) aesthetics of Web pages. First, multimodal features (structural, local visual, global visual, and functional) of a Web page that are known to significantly affect the aesthetics of a Web page are extracted to construct a feature vector. Second, the interuser disagreement of aesthetics is analyzed and novel aesthetic representations are obtained from the multiuser ratings of a page. A structural learning algorithm is proposed for the new aesthetic representations. Third, as a Web page's functional purpose also affects the perceived aesthetics, we divide Web pages into different types using functional features, and a soft multitask fusion learning strategy is introduced to train assessment models for pages with functional purposes. Experimental results show the effectiveness of our method: 1) the combination of structural, local, and global visual features outperforms existing state-of-the-art Web aesthetic features; 2) the proposed structural learning algorithm achieves good results for the new aesthetic representations; and 3) the proposed soft multitask fusion learning strategy improves the performances of aesthetics assessment models.
KeywordAesthetic Features Fusion Local Features Multitask Learning Visual Aesthetics Web Pages
WOS HeadingsScience & Technology ; Technology
DOI10.1109/TMM.2016.2538722
WOS KeywordQUALITY ASSESSMENT ; BACKGROUND COLOR ; IMAGES ; PAGES ; TEXT ; PERSPECTIVE ; EXPERIENCE ; FEATURES ; SEARCH ; USERS
Indexed BySCI
Language英语
Funding OrganizationNational Science Foundation China(61379098)
WOS Research AreaComputer Science ; Telecommunications
WOS SubjectComputer Science, Information Systems ; Computer Science, Software Engineering ; Telecommunications
WOS IDWOS:000376107100010
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12225
Collection模式识别国家重点实验室_视频内容安全
Corresponding AuthorOu Wu
Affiliation1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
2.China Univ Petr, Sch Engn, Qingdao 266580, Peoples R China
Recommended Citation
GB/T 7714
Wu, Ou,Zuo, Haiqiang,Hu, Weiming,et al. Multimodal Web Aesthetics Assessment Based on Structural SVM and Multitask Fusion Learning[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2016,18(6):1062-1076.
APA Wu, Ou,Zuo, Haiqiang,Hu, Weiming,Li, Bing,&Ou Wu.(2016).Multimodal Web Aesthetics Assessment Based on Structural SVM and Multitask Fusion Learning.IEEE TRANSACTIONS ON MULTIMEDIA,18(6),1062-1076.
MLA Wu, Ou,et al."Multimodal Web Aesthetics Assessment Based on Structural SVM and Multitask Fusion Learning".IEEE TRANSACTIONS ON MULTIMEDIA 18.6(2016):1062-1076.
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