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Two-Stage Semantic Matching for Cross-Media Retrieval

Volume 14, Number 4, April 2018, pp. 795-804
DOI: 10.23940/ijpe.18.04.p21.795804

Gongwen Xua, Lina Xua, Meijia Zhanga, and Xiaomei Lib

aSchool of Information Science and Engineering, Shandong Normal University, Jinan, 250358, China
bThe Second Hospital of Shandong University, Jinan, 250033, China

(Submitted on December 29, 2017; Revised on February 2, 2018; Accepted on March 20, 2018)


With the development of information technology, there exists a large amount of multi-media data in our lives; the data is heterogeneous with low-level features while consistent with semantic information. Traditional mono-media retrieval can’t cross the heterogeneous gap of multi-media data, and cross-media retrieval is arousing many researchers’ interests. In this paper, we propose a two-stage semantic matching for cross-media retrieval based on support vector machines (called TSMCR). Our approach uses a combination of testing images’ predictive labels and testing texts’ predictive labels as the next training labels. It makes full use of semantic information of both training samples and testing samples, and the experimental results on four state-of-the-art datasets show that the TSMCR algorithm is effective.


References: 9

  1. J. A. Bondy, U. S. R. Murty, “Graph Theory with Applications,” London: Macmillan, 1976.
  2. B. Cheng, J. Yang, S. Yan, Y. Fu and T.S. Huang, “Learning with L1-graph for Image Analysis,” IEEE Transactions on Image Processing, vol. 19, no. 4, pp. 858-866, 2010.
  3. C. Cortes, V. Vapnik, “Support-Vector Networks,” Machine Learning, vol. 20, no. 3, pp. 273-297, 1995.
  4. J.H. Holland, “Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence,” University of Michigan Press, Ann Arbor, Michigan, 1975.
  5. D. Howe, M. Costanzo, P. Fey, et al. “Big Data: The Future of Biocuration,” Nature, vol. 455, no. 7209, pp. 47-50, 2008.
  6. G.B. Huang, Q.Y. Zhu and C.K. Siew, “Extreme Learning Machine: Theory and Applications,” Neurocomputing, vol. 70, no. 1, pp. 489-501, 2006.
  7. W. McCulloch and W.H. Pitts, “A Logical Calculus of the Ideas Immanent in Nervous Activity,” The Bulletin of Mathematical Biophysics, vol. 5, no. 4, pp. 115-133, 1943.
  8. C. Olivier, S. Bernhard and Z. Alexander, “Semi-Supervised Learning,” MIT Press, Cambridge, USA, 2006.
  9. S.T. Roweis, L.K. Saul, “Nonlinear Dimensionality Reduction by Locally Linear Embedding,” Science, vol. 290, no. 5500, pp. 2323-2326, 2000.


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