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Multimedia Tools and Applications

, Volume 78, Issue 5, pp 6163–6190 | Cite as

Image retrieval based on effective feature extraction and diffusion process

  • Juxiang Zhou
  • Xiaodong LiuEmail author
  • Wanquan Liu
  • Jianhou Gan
Article

Abstract

Feature extraction and its matching are two critical tasks in image retrieval. This paper presents a new methodology for content-based image retrieval by integrating three features, and then optimizing feature metric by diffusion process. To boost the discriminative power, the color histogram, local directional pattern, and dense SIFT features based on bag of features (BoF) are selected. Then diffusion process is applied to seek a global optimization for image matching based on fused multi-features. The diffusion process can capture the intrinsic manifold structure on a dataset, and thus enhance the overall retrieval performance significantly. Finally, a new search strategy is explored to make the diffusion process work even better when the number of retrieval images is small. In order to validate our proposed approach, four benchmark databases are used, and the results of experiments show that the proposed approach outperforms all other existing approaches.

Keywords

Image retrieval Bag of features Diffusion processes 

Notes

Acknowledgements

We thank the anonymous reviewers and associate editor for their valuable comments that are invaluable in improving the quality of this paper. This work is supported by some grants from NSFC projects (Nos. 61673082, 61462097, 61602321, 61562093), and Application Infrastructure Projects of Science and Technology Plan in Yunnan Province (No.2016FD022).

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Copyright information

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

Authors and Affiliations

  • Juxiang Zhou
    • 1
    • 3
  • Xiaodong Liu
    • 1
    Email author
  • Wanquan Liu
    • 2
  • Jianhou Gan
    • 3
  1. 1.School of Control Science and EngineeringDalian University of TechnologyDalianChina
  2. 2.Department of ComputingCurtin UniversityPerthAustralia
  3. 3.Key Laboratory of Education Informatization for Nationalities, Ministry of EducationYunnan Normal UniversityKunmingChina

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