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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Machine Learning and Cybernetics
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
