Abstract
Cross-modal retrieval has been an active research topic in recent years. However, most existing methods ignored discovering the common semantic relationship among different modalities so as to seriously reduce the retrieval accuracy. To cope with this problem, we propose a novel cross-modal retrieval method based on coupled dictionary learning with common label alignment. Concretely, our method first conducts coupled dictionary learning on the data from different modalities separately and then projects them into a common space, where the correlation between these modalities is encouraged by using common label alignment. Experimental results on two public datasets demonstrate that our method outperforms several state-of-the-art methods.
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Acknowledgments
This work is supported by the National High Technology Research and Development Program of China (2013AA01A602), the Program for New Century Excellent Talents in University (NCET-12-0917), the Fundamental Research Funds for the Central Universities (No. K5051302019), the Key Science and Technology Program of Shaanxi Province, China (2014K05-16).
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Tang, X., Yang, Y., Deng, C., Gao, X. (2015). Coupled Dictionary Learning with Common Label Alignment for Cross-Modal Retrieval. In: He, X., et al. Intelligence Science and Big Data Engineering. Image and Video Data Engineering. IScIDE 2015. Lecture Notes in Computer Science(), vol 9242. Springer, Cham. https://doi.org/10.1007/978-3-319-23989-7_17
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DOI: https://doi.org/10.1007/978-3-319-23989-7_17
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