Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

7-2010

Abstract

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.

Keywords

Social Image Retrieval, Non-parametric Kernel Learning, Visual Ranking, Semidefinite Programming, Convex Optimization

Discipline

Computer Sciences | Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

CIVR '10: Proceedings of the ACM International Conference on Image and Video Retrieval: Xi'an, China, July 5-7

First Page

26

Last Page

33

ISBN

9781450301176

Identifier

10.1145/1816041.1816047

Publisher

ACM

City or Country

New York

Copyright Owner and License

Publisher

Additional URL

https://doi.org/10.1145/1816041.1816047

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