Click-Boosted Graph Ranking for Image Retrieval

Jun Wu1, 2, Yu He1, Xiaohong Qin1, Na Zhao2 and Yingpeng Sang3

  1. School of Computer and Information Technology, Beijing Jiaotong University
    Beijing 10044, China
    {wuj, 15120398, 14120420}@bjtu.edu.cn
  2. Logistics and E-commerce College, Zhejiang Wanli University
    Ningbo, 315100, China
    zhaona@zwu.edu.cn
  3. School of Information Science and Technology, Sun Yat-Sen University
    Guangzhou 510275, China
    sangyp@mail.sysu.edu.cn

Abstract

Graph ranking is one popular and successful technique for image retrieval, but its effectiveness is often limited by the well-known semantic gap. To bridge this gap, one of the current trends is to leverage the click-through data associated with images to facilitate the graph-based image ranking. However, the sparse and noisy properties of the image click-through data make the exploration of such resource challenging. Towards this end, this paper propose a novel click-boosted graph ranking framework for image retrieval, which consists of two coupled components. Concretely, the first one is a click predictor based on matrix factorization with visual regularization, in order to alleviate the sparseness of the click-through data. The second component is a soft-label graph ranker that conducts the image ranking by using the enriched click-through data noise-tolerantly. Extensive experiments for the tasks of click predicting and image ranking validate the effectiveness of the proposed methods in comparison to several existing approaches.

Key words

Image Retrieval, Click-Through Data, Graph Ranking, Matrix Factorization

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS170212020J

Publication information

Volume 14, Issue 3 (September 2017)
Advances in Information Technology, Distributed and Model Driven Systems
Year of Publication: 2017
ISSN: 1820-0214 (Print) 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Wu, J., He, Y., Qin, X., Zhao, N., Sang, Y.: Click-Boosted Graph Ranking for Image Retrieval. Computer Science and Information Systems, Vol. 14, No. 3, 629–641. (2017)