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Multimedia Tools and Applications

, Volume 78, Issue 5, pp 6033–6047 | Cite as

Sparse coding based few learning instances for image retrieval

  • Hao Wu
  • Rongfang Bie
  • Junqi Guo
  • Xin Meng
  • Shenling WangEmail author
Article
  • 213 Downloads

Abstract

Hundreds of thousands of images that are widely used in different fields of modern life have appeared in recent years. The process of retrieving the target images from a big database has become a meaningful problem. As one of the classical techniques of computer vision, image retrieval could effectively solve the problem. However, in most cases, high-quality retrieval results are supported by a large number of learning instances. It not only occupies much computing resources but also wastes much human resource. Moreover, much time is wasted in the process of retrieval. To solve the abovementioned problems, we proposed a sparse coding based few learning instances model for retrieval. Concretely, cross-validation sparse coding representation, sparse coding based instance distance and improved KNN model are combined which directly contributes to build up the previous model. It could reduce the number of learning instances significantly through the selection of optimized learning instances while preserving the retrieval accuracy. At last, a database using a large number of images was set up. The experimental results using the database show our method’s superiority in preserving the quality of retrieval with the reduction of learning instances.

Keywords

Image retrieval Cross-validation sparse coding representation Sparse coding based instance distance KNN AP value AUC value 

Notes

Acknowledgements

This research is sponsored by Fundamental Research Funds for the Central Universities (No.2016NT14), National Natural Science Foundation of China (No.61601033) and Beijing Advanced Innovation Center for Future Education (BJAICFE2016IR-004).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Hao Wu
    • 1
  • Rongfang Bie
    • 1
  • Junqi Guo
    • 1
  • Xin Meng
    • 2
  • Shenling Wang
    • 1
    Email author
  1. 1.College of Information Science and TechnologyBeijing Normal UniversityBeijingChina
  2. 2.Electric Power Planning & Engineering InstituteBeijingChina

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