• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Semi-supervised Cross-media Feature Learning via Efficient L2,q Norm

Vol. 13, No.3, March 31, 2019
10.3837/tiis.2019.03.016, Download Paper (Free):

Abstract

With the rapid growth of multimedia data, research on cross-media feature learning has significance in many applications, such as multimedia search and recommendation. Existing methods are sensitive to noise and edge information in multimedia data. In this paper, we propose a semi-supervised method for cross-media feature learning by means of L2,q norm to improve the performance of cross-media retrieval, which is more robust and efficient than the previous ones. In our method, noise and edge information have less effect on the results of cross-media retrieval and the dynamic patch information of multimedia data is employed to increase the accuracy of cross-media retrieval. Our method can reduce the interference of noise and edge information and achieve fast convergence. Extensive experiments on the XMedia dataset illustrate that our method has better performance than the state-of-the-art methods.


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Cite this article

[IEEE Style]
Zhikai Zong, Aili Han and Qing Gong, "Semi-supervised Cross-media Feature Learning via Efficient L2,q Norm," KSII Transactions on Internet and Information Systems, vol. 13, no. 3, pp. 1403-1417, 2019. DOI: 10.3837/tiis.2019.03.016

[ACM Style]
Zong, Z., Han, A., and Gong, Q. 2019. Semi-supervised Cross-media Feature Learning via Efficient L2,q Norm. KSII Transactions on Internet and Information Systems, 13, 3, (2019), 1403-1417. DOI: 10.3837/tiis.2019.03.016