Vlfeat: an open and portable library of computer vision algorithms. 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.

References in zbMATH (referenced in 20 articles )

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  1. de Amorim, Renato Cordeiro: A survey on feature weighting based K-means algorithms (2016)
  2. Ramírez-Corona, Mallinali; Sucar, L.Enrique; Morales, Eduardo F.: Hierarchical multilabel classification based on path evaluation (2016)
  3. Rey-Otero, Ives; Morel, Jean-Michel; Delbracio, Mauricio: An analysis of the factors affecting keypoint stability in scale-space (2016)
  4. Farhan, Erez; Hagege, Rami: Geometric expansion for local feature analysis and matching (2015)
  5. Fedorov, Vadim; Arias, Pablo; Sadek, Rida; Facciolo, Gabriele; Ballester, Coloma: Linear multiscale analysis of similarities between images on Riemannian manifolds: practical formula and affine covariant metrics (2015)
  6. Bilen, Hakan; Namboodiri, Vinay P.; van Gool, Luc J.: Object and action classification with latent window parameters (2014)
  7. Collins, Toby; Bartoli, Adrien: Infinitesimal plane-based pose estimation (2014)
  8. El Bouti, Tamara; Mercier, Gwenael; Obrecht, Caroline; Benedetti, Giuseppe: Detection of an image in a video sequence (2014)
  9. Hoai, Minh; Torresani, Lorenzo; De la Torre, Fernando; Rother, Carsten: Learning discriminative localization from weakly labeled data (2014)
  10. Lei, Hao; Mei, Kuizhi; Zheng, Nanning; Dong, Peixiang; Zhou, Ning; Fan, Jianping: Learning group-based dictionaries for discriminative image representation (2014)
  11. Sapienza, Michael; Cuzzolin, Fabio; Torr, Philip H.S.: Learning discriminative space-time action parts from weakly labelled videos (2014)
  12. Tran, Quoc Huy; Chin, Tat-Jun; Chojnacki, Wojciech; Suter, David: Sampling minimal subsets with large spans for robust estimation (2014)
  13. Ma, Jiayi; Zhao, Ji; Tian, Jinwen; Bai, Xiang; Tu, Zhuowen: Regularized vector field learning with sparse approximation for mismatch removal (2013)
  14. Qian, Jianjun; Yang, Jian; Gao, Guangwei: Discriminative histograms of local dominant orientation (D-HLDO) for biometric image feature extraction (2013)
  15. Wang, Jim Jing-Yan; Bensmail, Halima; Gao, Xin: Joint learning and weighting of visual vocabulary for bag-of-feature based tissue classification (2013)
  16. Albarelli, Andrea; Rodolà, Emanuele; Torsello, Andrea: Imposing semi-local geometric constraints for accurate correspondences selection in structure from motion: a game-theoretic perspective (2012)
  17. Cruz-Mota, Javier; Bogdanova, Iva; Paquier, Beno^ıt; Bierlaire, Michel; Thiran, Jean-Philippe: Scale invariant feature transform on the sphere: theory and applications (2012)
  18. Dalalyan, Arnak; Keriven, Renaud: Robust estimation for an inverse problem arising in multiview geometry (2012)
  19. Nock, Richard; Piro, Paolo; Nielsen, Frank; Ali, Wafa Bel Haj; Barlaud, Michel: Boosting $k$-NN for categorization of natural scenes (2012)
  20. Mei, Christopher; Sibley, Gabe; Cummins, Mark; Newman, Paul; Reid, Ian: RSLAM: a system for large-scale mapping in constant-time using stereo (2011)