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Image Matching from Handcrafted to Deep Features: A Survey

Open access
Ma, Jiayi, Jiang, Xingyu, Fan, Aoxiang, Jiang, Junjun, Yan, Junchi·2021·International Journal of Computer Vision
DOI 10.1007/s11263-020-01359-2
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Abstract

As a fundamental and critical task in various visual applications, image matching can identify then correspond the same or similar structure/content from two or more images. Over the past decades, growing amount and diversity of methods have been proposed for image matching, particularly with the development of deep learning techniques over the recent years. However, it may leave several open questions about which method would be a suitable choice for specific applications with respect to different scenarios and task requirements and how to design better image matching methods with superior performance in accuracy, robustness and efficiency. This encourages us to conduct a comprehensive and systematic review and analysis for those classical and latest techniques. Following the feature-based image matching pipeline, we first introduce feature detection, description, and matching techniques from handcrafted methods to trainable ones and provide an analysis of the development of these methods in theory and practice. Secondly, we briefly introduce several typical image matching-based applications for a comprehensive understanding of the significance of image matching. In addition, we also provide a comprehensive and objective comparison of these classical and latest techniques through extensive experiments on representative datasets. Finally, we conclude with the current status of image matching technologies and deliver insightful discussions and prospects for future works. This survey can serve as a reference for (but not limited to) researchers and engineers in image matching and related fields.

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Metadata

Authors
Ma, J., Jiang, X., Fan, A., Jiang, J., & Yan, J.
Venue
International Journal of Computer Vision
Publisher
Springer Science and Business Media LLC
Published
2020-08-04
Volume
129
Issue
1
Pages
23-79
ISSN
  • 0920-5691 (ISSN-L)
  • 1573-1405
DOI
10.1007/s11263-020-01359-2
OpenAlex
W3047057232
Cited by
990
References
497
FWCI
51.30
Type
journal article
Language
en
Open access
hybrid
Licence
Funders
  • National Natural Science Foundation of China (61773295)
  • Natural Science Foundation of Hubei Province (2019CFA037)
  • Key Technologies Research and Development Program (2018AAA0100704)

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CoChat. (2026). Citation check: Image Matching from Handcrafted to Deep Features: A Survey. https://app.cochat.ai/papers/10.1007/s11263-020-01359-2
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Ma, J., Jiang, X., Fan, A., Jiang, J., & Yan, J. (2021). Image Matching from Handcrafted to Deep Features: A Survey. International Journal of Computer Vision. https://doi.org/10.1007/s11263-020-01359-2

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