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A hybrid image retrieval system for microscopy images

Published online by Cambridge University Press:  30 July 2021

Weixin Jiang ,
Eric Schwenker ,
Trevor Spreadbury ,
Oliver Cossairt  and
Maria KY Chan
Weixin Jiang
Affiliation:
Northwestern University, United States
Eric Schwenker
Affiliation:
Argonne National Laboratory, United States
Trevor Spreadbury
Affiliation:
Argonne National Laboratory, United States
Oliver Cossairt
Affiliation:
Northwestern University, United States
Maria KY Chan
Affiliation:
ANL, United States

Abstract

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Type
Full System and Workflow Automation for Enabling Big Data and Machine Learning in Electron Microscopy
Information
Microscopy and Microanalysis , Volume 27 , Supplement S1 , August 2021 , pp. 474 - 476
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Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of the Microscopy Society of America

References

Schwenker, E., Jiang, W., Spreadbury, T., O'Brien, S., Ferrier, N., Cossairt, O., & Chan, M. (2020). Constructing Self-Labeled Materials Imaging Datasets from Open Access Scientific Journals with EXSCLAIM! Microscopy and Microanalysis, 26(S2), 3096–3097. doi:10.1017/S1431927620023806CrossRefGoogle Scholar
Jiang, W., Schwenker, E., Spreadbury, T., Ferrier, N., Chan, M. K., & Cossairt, O. (2021). A Two-stage Framework for Compound Figure Separation. arXiv preprint arXiv:2101.09903.Google Scholar
Radenović, F., Tolias, G., & Chum, O. (2018). Fine-tuning CNN image retrieval with no human annotation. IEEE transactions on pattern analysis and machine intelligence, 41(7), 1655–1668.CrossRefGoogle ScholarPubMed
Radenović, F., Tolias, G., & Chum, O. (2016, October). CNN image retrieval learns from BoW: Unsupervised fine-tuning with hard examples. In European conference on computer vision (pp. 3–20). Springer, Cham.CrossRefGoogle Scholar
Sanderson, M., & Croft, W. B. (2012). The history of information retrieval research. Proceedings of the IEEE, 100(Special Centennial Issue), 1444–1451.CrossRefGoogle Scholar
Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. arXiv preprint arXiv:1506.01497.Google Scholar
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770–778)CrossRefGoogle Scholar
Zheng, L., Yang, Y., & Tian, Q. (2017). SIFT meets CNN: A decade survey of instance retrieval. IEEE transactions on pattern analysis and machine intelligence, 40(5), 1224–1244.Google Scholar
Hu, J., Lu, J., & Tan, Y. P. (2014). Discriminative deep metric learning for face verification in the wild. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1875–1882).Google Scholar
Hoffer, E., & Ailon, N. (2015, October). Deep metric learning using triplet network. In International workshop on similarity-based pattern recognition (pp. 84–92). Springer, Cham.CrossRefGoogle Scholar