The DOI matches its Crossref record; 47 of the 47 references it cites were found in a registry or an index.
Discriminative Learning of Local Image Descriptors
Open accessAbstract
In this paper, we explore methods for learning local image descriptors from training data. We describe a set of building blocks for constructing descriptors which can be combined together and jointly optimized so as to minimize the error of a nearest-neighbor classifier. We consider both linear and nonlinear transforms with dimensionality reduction, and make use of discriminant learning techniques such as Linear Discriminant Analysis (LDA) and Powell minimization to solve for the parameters. Using these techniques, we obtain descriptors that exceed state-of-the-art performance with low dimensionality. In addition to new experiments and recommendations for descriptor learning, we are also making available a new and realistic ground truth data set based on multiview stereo data.
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Cites
Distinctive Image Features from Scale-Invariant Keypoints
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A performance evaluation of local descriptors
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Scalable Recognition with a Vocabulary Tree
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Scale & Affine Invariant Interest Point Detectors
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Cited by
Structure-from-Motion Revisited
Schonberger, J. L., & Frahm, J.-M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.445 · Cited by 6,433
Receptive Field Block Net for Accurate and Fast Object Detection
Liu, S., Huang, D., & Wang, Y. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01252-6_24 · Cited by 1,885
Learning to compare image patches via convolutional neural networks
Zagoruyko, S., & Komodakis, N. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7299064 · Cited by 1,140
LDAHash: Improved Matching with Smaller Descriptors
Strecha, C., Bronstein, A. M., Bronstein, M. M., & Fua, P. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.103 · Cited by 605
Image matching across wide baselines: From paper to practice
Jin, Y., Mishkin, D., Mishchuk, A., Matas, J., Fua, P., Yi, K. M., & Trulls, E. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-020-01385-0 · Cited by 388
Metadata
- Authors
- Brown, M., Hua, G., & Winder, S.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2011-01
- Volume
- 33
- Issue
- 1
- Pages
- 43-57
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2089888558
- Cited by
- 466
- References
- 47
- FWCI
- 26.54
- Type
- journal article
- Language
- en
- Open access
- green
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