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Multimedia Tools and Applications

, Volume 74, Issue 2, pp 561–576 | Cite as

Accumulated reconstruction error vector (AREV): a semantic representation for cross-media retrieval

  • Kai Liu
  • Shikui WeiEmail author
  • Yao Zhao
  • Zhenfeng Zhu
  • Yunchao Wei
  • Changsheng Xu
Article
  • 348 Downloads

Abstract

Cross-media retrieval aims to automatically perform the content-based search procedure among various media types (e.g., image, video and text), in which media representation plays an important role for providing the heterogeneous similarity measure. In this work, a novel semantic representation of cross-media, called accumulated reconstruction error vector (AREV), is proposed, which includes category-specific dictionary learning, media sample reconstruction, and accumulative reconstruction error concatenation. Instead of directly learning the correlation relationship among heterogeneous items in the same semantic groups, the AREV projects individually their original feature descriptions into a shared semantic space, in which each component is semantic consistent for various media types due to the consistency in category information. Experiments on the commonly used datasets, i.e. Wikipedia dataset and NUS-Wide dataset, show the good performance in terms of effectiveness and efficiency.

Keywords

Cross-media Accumulated reconstruction error vector Retrieval Consistency Dictionary learning 

Notes

Acknowledgments

This work was supported in part by the 973 Program (No. 2012CB316400), PCSIRT (No.IRT201206), the National Science Foundation of China (No.61202241, No.61210006, and No.61025013), the Fundamental Research Funds for the Central Universities (No.2013JBM024), and the Open Project Program of the National Laboratory of Pattern Recognition (NLPR).

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Kai Liu
    • 1
    • 3
  • Shikui Wei
    • 1
    • 3
    Email author
  • Yao Zhao
    • 1
    • 3
  • Zhenfeng Zhu
    • 1
    • 3
  • Yunchao Wei
    • 1
    • 3
  • Changsheng Xu
    • 2
  1. 1.Institute of Information ScienceBeijing Jiaotong UniversityBeijingChina
  2. 2.Institute of AutomationChinese Academy of SciencesBeijingChina
  3. 3.Beijing Key Laboratory of Advanced Information Science and Network TechnologyBeijingChina

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