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Conference Paper Multi-stage Image Retrieval based on Feature Augmentation with Truncated Polynomial Weight
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Authors
Keundong Lee, Seungjae Lee, Wonyoung Yoo
Issue Date
2018-10
Citation
International Conference on Information and Communication Technology Convergence (ICTC) 2018, pp.480-483
Publisher
IEEE
Language
English
Type
Conference Paper
DOI
https://dx.doi.org/10.1109/ICTC.2018.8539691
Abstract
In this paper, we propose an effective image retrieval method. Based on a conventional global image representation, multi-stage image retrieval pipeline with feature augmentation is constructed to improve retrieval accuracy. To suppress irrelevant images while boosting relevant images, a novel weighting scheme for feature augmentation is introduced. In addition, the relationship between database images is leveraged to update or re-rank shortlist of the retrieved images. The proposed method was evaluated on Google-landmarks dataset, and the experimental results validate the effectiveness of the proposed method.
KSP Keywords
Feature augmentation, Image Representation, Image retrieval, Multi-stage, Re-rank, Weighting scheme, retrieval accuracy, retrieval method