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05-12-2018 | Original Article | Issue 7/2019

International Journal of Machine Learning and Cybernetics 7/2019

Multi-center convolutional descriptor aggregation for image retrieval

Journal:
International Journal of Machine Learning and Cybernetics > Issue 7/2019
Authors:
Jie Zhu, Shufang Wu, Hong Zhu, Yan Li, Li Zhao
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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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