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An image reranking algorithm based on discrete-time quantum walk

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Abstract

In order to further improve the performance of image retrieval, a novel image reranking algorithm based on discrete-time quantum walk is proposed. In this algorithm, a discrete-time quantum walk model based on a weighted undirected complete graph is first constructed, in which the nodes of the graph represent the images and the weighted values of these edges are the similarity value between the images. Then the average probability values of the walker reaching the node of the graph is used as the relevance scores of the image and the images are reranked according to the relevance scores. Finally, our experimental results show that our proposed reranking algorithm has a significant improvement compared with the initial ranking algorithm from the comparison of visual and relevance scores. Furthermore, the effectiveness of our algorithm is evaluated by the average precision (AP) and the mean average precision (MAP), where the AP of our algorithm is increased by 18.23% and 37.61% for two types of the query image in randomly selected image group respectively, and the MAP of our algorithm is increased by 22.24% for all image groups compared with the initial ranking algorithm.

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Acknowledgements

This work is supported by The National Natural Science Foundation of China (No. 61602019); Beijing Natural Science Foundation (Grant No.4182006).

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Authors and Affiliations

  1. College of Computer Science and Technology, Beijing University of Technology, Beijing, 100124, China

    Wei-Min Shi, Qing-Tian Zhuang,  Xue-Zhang, Yi-Hua Zhou & Yu-Guang Yang

  2. Beijing Key Laboratory of Trusted Computing, Beijing, 100124, China

    Wei-Min Shi, Qing-Tian Zhuang,  Xue-Zhang, Yi-Hua Zhou & Yu-Guang Yang

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  1. Wei-Min Shi
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  2. Qing-Tian Zhuang
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  3. Xue-Zhang
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  4. Yi-Hua Zhou
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Correspondence to Wei-Min Shi.

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Shi, WM., Zhuang, QT., Xue-Zhang et al. An image reranking algorithm based on discrete-time quantum walk. Multimed Tools Appl 83, 34979–34994 (2024). https://doi.org/10.1007/s11042-023-16916-3

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  • DOI: https://doi.org/10.1007/s11042-023-16916-3

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