Visual-Textual Joint Relevance Learning for Tag-Based Social Image Search | |
Gao, Yue1; Wang, Meng2; Zha, Zheng-Jun3; Shen, Jialie4; Li, Xuelong5; Wu, Xindong6,7 | |
2013 | |
发表期刊 | IEEE TRANSACTIONS ON IMAGE PROCESSING |
卷号 | 22期号:1页码:363-376 |
摘要 | Due to the popularity of social media websites, extensive research efforts have been dedicated to tag-based social image search. Both visual information and tags have been investigated in the research field. However, most existing methods use tags and visual characteristics either separately or sequentially in order to estimate the relevance of images. In this paper, we propose an approach that simultaneously utilizes both visual and textual information to estimate the relevance of user tagged images. The relevance estimation is determined with a hypergraph learning approach. In this method, a social image hypergraph is constructed, where vertices represent images and hyperedges represent visual or textual terms. Learning is achieved with use of a set of pseudo-positive images, where the weights of hyperedges are updated throughout the learning process. In this way, the impact of different tags and visual words can be automatically modulated. Comparative results of the experiments conducted on a dataset including 370+ images are presented, which demonstrate the effectiveness of the proposed approach. |
文章类型 | Article |
关键词 | Hypergraph Learning Social Image Search Tag Visual-textual |
WOS标题词 | Science & Technology ; Technology |
收录类别 | SCI ; EI |
关键词[WOS] | HYPER-GRAPHS ; RETRIEVAL ; RECOGNITION ; FEATURES |
语种 | 英语 |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000312892000029 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.opt.ac.cn/handle/181661/23196 |
专题 | 光谱成像技术研究室 |
作者单位 | 1.Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China 2.Hefei Univ Technol, Comp Sci & Informat Engn Dept, Hefei 230009, Peoples R China 3.Natl Univ Singapore, Sch Comp, Singapore 117417, Singapore 4.Singapore Management Univ, Sch Informat Syst, Singapore 178902, Singapore 5.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Beijing 100864, Peoples R China 6.Hefei Univ Technol, Sch Comp Sci & Informat Engn, Hefei 230009, Peoples R China 7.Univ Vermont, Dept Comp Sci, Burlington, VT 05405 USA |
推荐引用方式 GB/T 7714 | Gao, Yue,Wang, Meng,Zha, Zheng-Jun,et al. Visual-Textual Joint Relevance Learning for Tag-Based Social Image Search[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2013,22(1):363-376. |
APA | Gao, Yue,Wang, Meng,Zha, Zheng-Jun,Shen, Jialie,Li, Xuelong,&Wu, Xindong.(2013).Visual-Textual Joint Relevance Learning for Tag-Based Social Image Search.IEEE TRANSACTIONS ON IMAGE PROCESSING,22(1),363-376. |
MLA | Gao, Yue,et al."Visual-Textual Joint Relevance Learning for Tag-Based Social Image Search".IEEE TRANSACTIONS ON IMAGE PROCESSING 22.1(2013):363-376. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Visual-Textual Joint(2269KB) | 期刊论文 | 出版稿 | 限制开放 | CC BY | 请求全文 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论