The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
MatchNet: Unifying feature and metric learning for patch-based matching
Abstract
Motivated by recent successes on learning feature representations and on learning feature comparison functions, we propose a unified approach to combining both for training a patch matching system. Our system, dubbed Match-Net, consists of a deep convolutional network that extracts features from patches and a network of three fully connected layers that computes a similarity between the extracted features. To ensure experimental repeatability, we train MatchNet on standard datasets and employ an input sampler to augment the training set with synthetic exemplar pairs that reduce overfitting. Once trained, we achieve better computational efficiency during matching by disassembling MatchNet and separately applying the feature computation and similarity networks in two sequential stages. We perform a comprehensive set of experiments on standard datasets to carefully study the contributions of each aspect of MatchNet, with direct comparisons to established methods. Our results confirm that our unified approach improves accuracy over previous state-of-the-art results on patch matching datasets, while reducing the storage requirement for descriptors. We make pre-trained MatchNet publicly available.
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Self-citationPCA-SIFT: a more distinctive representation for local image descriptors
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Self-citationA comparison of affine region detectors
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Matterport3D: Learning from RGB-D Data in Indoor Environments
Chang, A., Dai, A., Funkhouser, T., Halber, M., Niebner, M., Savva, M., Song, S., Zeng, A., & Zhang, Y. (2017) · 2017 International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2017.00081 · Cited by 1,858
MVSNet: Depth Inference for Unstructured Multi-view Stereo
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Siamese Instance Search for Tracking
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Metadata
- Authors
- Han, X., Leung, T., Jia, Y., Sukthankar, R., & Berg, A. C.
- Venue
- 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2015-06
- Pages
- 3279-3286
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W1929856797
- Cited by
- 825
- References
- 11
- FWCI
- 36.44
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Han, X., Leung, T., Jia, Y., Sukthankar, R., & Berg, A. C. (2015). MatchNet: Unifying feature and metric learning for patch-based matching. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2015.7298948
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