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Multimedia Tools and Applications

, Volume 77, Issue 1, pp 1437–1452 | Cite as

Enhancing heterogeneous similarity estimation via neighborhood reversibility

  • Shikui WeiEmail author
  • Yao Zhao
  • Tao Yang
  • Zhili Zhou
  • Shiming Ge
Article

Abstract

With the popularity of social networks, people can easily generate rich content with multiple modalities. How to effectively and simply estimate the similarity of multi-modal content is becoming more and more important for providing better information searching service of rich media. This work attempts to enhance the similarity estimation so as to improve the accuracy of multi-modal data searching. Toward this end, a novel multi-modal feature extraction approach, which involves the neighborhood reversibility verifying of information objects with different modalities, is proposed to build reliable similarity estimation among multimedia documents. By verifying the neighborhood reversibility in both single- and multi-modal instances, the reliability of multi-modal subspace can be remarkably improved. In addition, a new adaptive strategy, which fully employs the distance distribution of returned searching instances, is proposed to handle the neighbor selection problem. To further address the out-of-sample problem, a new prediction scheme is proposed to predict the multi-modal features for new coming instances, which is essentially to construct an over-complete set of bases. Extensive experiments demonstrate that introducing the neighborhood reversibility verifying can significantly improve the searching accuracy of multi-modal documents.

Keywords

Neighbourhood reversibility verifying Multi-modal retrieval Adaptive strategy 

Notes

Acknowledgments

This work was supported in part by National Natural Science Foundation of China (No.61572065, No.61532005), Joint Fund of Ministry of Education of China and China Mobile (No.MCM20160102), and Fundamental Research Funds for the Central Universities (No.2015JBM028, No.2015JBZ002).

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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Institute of Information ScienceBeijing Jiaotong UniversityBeijingChina
  2. 2.School of Computer and SoftwareNanjing University of Information Science and TechnologyNanjingChina
  3. 3.Institute of Information EngineeringChinese Academy of SciencesBeijingChina

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