The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Vlfeat: an open and portable library of computer vision algorithms
Abstract
VLFeat is an open and portable library of computer vision algorithms. It aims at facilitating fast prototyping and reproducible research for computer vision scientists and students. It includes rigorous implementations of common building blocks such as feature detectors, feature extractors, (hierarchical) k-means clustering, randomized kd-tree matching, and super-pixelization. The source code and interfaces are fully documented. The library integrates directly with MATLAB, a popular language for computer vision research.
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Cites
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
Lazebnik, S., Schmid, C., & Ponce, J. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06) · doi:10.1109/cvpr.2006.68 · Cited by 7,916
Video Google: a text retrieval approach to object matching in videos
Sivic, & Zisserman (2003) · Proceedings Ninth IEEE International Conference on Computer Vision · doi:10.1109/iccv.2003.1238663 · Cited by 6,367
Robust Wide Baseline Stereo from Maximally Stable Extremal Regions
Matas, J., Chum, O., Urban, M., & Pajdla, T. (2002) · Procedings of the British Machine Vision Conference 2002 · doi:10.5244/c.16.36 · Cited by 1,350
Cited by
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
“Both of them are powerful shallow representations for image retrieval and classification [4, 47].”
BackgroundDeep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1512.03385 · Cited by 4,196
“Both of them are powerful shallow representations for image retrieval and classification [4, 47].”
BackgroundMatConvNet: Convolutional Neural Networks for MATLAB
Vedaldi, A., & Lenc, K. (2015) · Proceedings of the 23rd ACM international conference on Multimedia · doi:10.1145/2733373.2807412 · Cited by 2,444
Self-citationUnderstanding bag-of-words model: a statistical framework
Zhang, Y., Jin, R., & Zhou, Z.-H. (2010) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-010-0001-0 · Cited by 1,384
Framing Image Description as a Ranking Task: Data, Models and Evaluation Metrics
Hodosh, M., Young, P., & Hockenmaier, J. (2013) · Journal of Artificial Intelligence Research · doi:10.1613/jair.3994 · Cited by 1,314
Metadata
- Authors
- Vedaldi, A., & Fulkerson, B.
- Venue
- Proceedings of the 18th ACM international conference on Multimedia
- Publisher
- ACM
- Published
- 2010-10-25
- Pages
- 1469-1472
- OpenAlex
- W2066941820
- Cited by
- 2,664
- References
- 4
- FWCI
- 149.82
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Vedaldi, A., & Fulkerson, B. (2010). Vlfeat: an open and portable library of computer vision algorithms. Proceedings of the 18th ACM international conference on Multimedia. https://doi.org/10.1145/1873951.1874249
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