Visual-textual joint relevance learning for tag-based social image search.
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.
Department of Automation, Tsinghua University, Beijing 100084, China.
Yue
Y
Gao
Meng
M
Wang
Zheng-Jun
ZJ
Zha
Jialie
J
Shen
Xuelong
X
Li
Xindong
X
Wu
D000465:Algorithms; D000818:Animals; D016208:Databases, Factual; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D010781:Photography / Q000379:methods*; D061108:Social Media