Conference Proceeding Article
Social image retrieval has become an emerging research challenge in web rich media search. In this paper, we address the research problem of text-based social image retrieval, which aims to identify and return a set of relevant social images that are related to a text-based query from a corpus of social images. Regular approaches for social image retrieval simply adopt typical text-based image retrieval techniques to search for the relevant social images based on the associated tags, which may suffer from noisy tags. In this paper, we present a novel framework for social image re-ranking based on a non-parametric kernel learning technique, which explores both textual and visual contents of social images for improving the ranking performance in social image retrieval tasks. Unlike existing methods that often adopt some fixed parametric kernel function, our framework learns a non-parametric kernel matrix that can effectively encode the information from both visual and textual domains. Although the proposed learning scheme is transductive, we suggest some solution to handle unseen data by warping the non-parametric kernel space to some input kernel function. Encouraging experimental results on a real-world social image testbed exhibit the effectiveness of the proposed method.
Computer Sciences | Databases and Information Systems
Data Management and Analytics
CIVR '10: Proceedings of the ACM International Conference on Image and Video Retrieval: Xi'an, China, July 5-7, 2010
City or Country
ZHUANG, Jinfeng and HOI, Steven C. H..
Nonparametric Kernel Ranking Approach for Social Image Retrieval. (2010). CIVR '10: Proceedings of the ACM International Conference on Image and Video Retrieval: Xi'an, China, July 5-7, 2010. 26-33. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2364
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.