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Multi-center convolutional descriptor aggregation for image retrieval

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Abstract

Recent works have demonstrated that the convolutional descriptor aggregation can provide state-of-the-art performance for image retrieval. In this paper, we propose a multi-center convolutional descriptor aggregation (MCDA) method to produce global image representation for image retrieval. We first present a feature map center selection method to eliminate the background information in the feature maps. We then propose the channel weighting and spatial weighting schemes based on the centers to boost the effect of the features on the object. Finally, the weighted convolutional descriptors are aggregated to represent images. Experiments demonstrate that MCDA can produce state-of-the-art retrieval performance, and the generated activation map is also effective for object localization.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant 61772344 and Grant 61732011), in part by the Natural Science Foundation of SZU (Grant 827-000140, Grant 827-000230, and Grant 2017060), the National Social Science Foundation of China (17BTQ068), the Youth Foundation of Education Bureau of Hebei Province (Grant QN2015099), China Postdoctoral Science Foundation funded project (Grant 2017M621078). The funding project of midwest colleges and universities promoting comprehensive strength of Hebei University.

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Authors and Affiliations

  1. Department of Information Management, The National Police University for Criminal Justice, Baoding, China

    Jie Zhu

  2. College of Management, Hebei University, Baoding, China

    Shufang Wu

  3. College of Computer Science and Software Engineering, Shen Zhen University, Shenzhen, China

    Hong Zhu & Li Zhao

  4. School of Applied Mathematics, Beijing Normal University Zhuhai, Zhuhai, China

    Yan Li

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  1. Jie Zhu
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  2. Shufang Wu
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  3. Hong Zhu
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  4. Yan Li
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  5. Li Zhao
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Correspondence to Shufang Wu.

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Zhu, J., Wu, S., Zhu, H. et al. Multi-center convolutional descriptor aggregation for image retrieval. Int. J. Mach. Learn. & Cyber. 10, 1863–1873 (2019). https://doi.org/10.1007/s13042-018-0898-2

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