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Image retrieval based on effective feature extraction and diffusion process

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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.

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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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Authors and Affiliations

  1. School of Control Science and Engineering, Dalian University of Technology, Dalian, 116024, China

    Juxiang Zhou & Xiaodong Liu

  2. Department of Computing, Curtin University, Perth, WA, 6102, Australia

    Wanquan Liu

  3. Key Laboratory of Education Informatization for Nationalities, Ministry of Education, Yunnan Normal University, Kunming, 650500, China

    Juxiang Zhou & Jianhou Gan

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  1. Juxiang Zhou
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  2. Xiaodong Liu
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Correspondence to Xiaodong Liu.

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Zhou, J., Liu, X., Liu, W. et al. Image retrieval based on effective feature extraction and diffusion process. Multimed Tools Appl 78, 6163–6190 (2019). https://doi.org/10.1007/s11042-018-6192-1

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