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International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 7/2019

05-12-2018 | Original Article

Multi-center convolutional descriptor aggregation for image retrieval

Authors: Jie Zhu, Shufang Wu, Hong Zhu, Yan Li, Li Zhao

Published in: International Journal of Machine Learning and Cybernetics | Issue 7/2019

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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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Metadata
Title
Multi-center convolutional descriptor aggregation for image retrieval
Authors
Jie Zhu
Shufang Wu
Hong Zhu
Yan Li
Li Zhao
Publication date
05-12-2018
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 7/2019
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-018-0898-2

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