Gradually focused fine-grained sketch-based image retrieval

PloS One
Ming ZhuWenxia Bao

Abstract

This paper focuses on fine-grained image retrieval based on sketches. Sketches capture detailed information, but their highly abstract nature makes visual comparisons with images more difficult. In spite of the fact that the existing models take into account the fine-grained details, they can not accurately highlight the distinctive local features and ignore the correlation between features. To solve this problem, we design a gradually focused bilinear attention model to extract detailed information more effectively. Specifically, the attention model is to accurately focus on representative local positions, and then use the weighted bilinear coding to find more discriminative feature representations. Finally, the global triplet loss function is used to avoid oversampling or undersampling. The experimental results show that the proposed method outperforms the state-of-the-art sketch-based image retrieval methods.

References

Feb 7, 2007·Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society·Shao HongTang Li
Dec 3, 2014·Neural Networks : the Official Journal of the International Neural Network Society·Tara N SainathBhuvana Ramabhadran
Dec 17, 2015·IEEE/ACM Transactions on Computational Biology and Bioinformatics·Hao ZhangXiaohui Lin
Jan 6, 2017·IEEE Transactions on Pattern Analysis and Machine Intelligence·Feng ZhengLing Shao
Sep 1, 2017·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society· Ke Li Honggang Zhang
Jun 6, 2018·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Feng Zheng, Ling Shao
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