- [1]
R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Susstrunk. SLIC superpixels. Technical report, EPFL, 2010.
- [2]
R. Arandjelovic and A. Zisserman. All about VLAD. In Proc. CVPR, 2013.
- [3]
D. Arthur and S. Vassilvitskii.
k-means++: The advantages of careful seeding. In Proc. ACM-SIAM Symp. on Discrete Algorithms, 2007.- [4]
J. S. Beis and D. G. Lowe. Shape indexing using approximate nearest-neighbour search in high-dimensional spaces. In Proc. CVPR, 1997.
- [5]
A. P. Dempster, N. M. Laird, and D. B. Rubin. Maximum likelihood from incomplete data via the em algorithm. Journal Of The Royal Statistical Society, 39(1):1–38, 1977.
- [6]
C. Elkan. Using the triangle inequality to accelerate k-means. In Proc. ICML, 2003.
- [7]
C.-J. Hsieh, K.-W. Chang, C.-J. Lin, S. S. Keerthi, and S. Sundararajan. A dual coordinate descent method for large-scale linear svm. In Proc. ICML, 2008.
- [8]
T. S. Jaakkola and D. Haussler. Exploiting generative models in discriminative classifiers. In Proc. NIPS, 1998.
- [9]
H. Jegou, M. Douze, C. Schmid, and P. Perez. Aggregating local descriptors into a compact image representation. In Proc. CVPR, 2010.
- [10]
J. Koenderink. The structure of images. Biological Cybernetics, 50, 1984.
- [11]
T. Lindeberg. Scale-Space Theory in Computer Vision. Springer, 1994.
- [12]
T. Lindeberg. Principles for automatic scale selection. Technical Report ISRN KTH/NA/P 98/14 SE, Royal Institute of Technology, 1998.
- [13]
S. Lloyd. Least square quantization in PCM. IEEE Trans. on Information Theory, 28(2), 1982.
- [14]
D. G. Lowe. Object recognition from local scale-invariant features. In Proc. ICCV, 1999.
- [15]
J. Matas, O. Chum, M. Urban, and T. Pajdla. Robust wide baseline stereo from maximally stable extremal regions. In Proc. BMVC, 2002.
- [16]
M. Matsumoto and T. Nishimura. Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator. ACM Trans. Model. Comput. Simul., 8(1), 1998.
- [17]
M. Muja and D. G. Lowe. Fast approximate nearest neighbors with automatic algorithmic configuration. In Proc. VISAPP, 2009.
- [18]
F. Perronnin and C. Dance. Fisher kenrels on visual vocabularies for image categorizaton. In Proc. CVPR, 2006.
- [19]
Florent Perronnin, Jorge Sánchez, and Thomas Mensink. Improving the fisher kernel for large-scale image classification. In Proc. ECCV, 2010.
- [20]
S. Shalev-Shwartz and T. Zhang. Stochastic dual coordinate ascent methods for regularized loss minimization. Number arXiv:1209.1873v2, 2013.
- [21]
S. Shalev-Shwartz, Y. Singer, and N. Srebro. Pegasos: Primal estimated sub-GrAdient SOlver for SVM. In Proc. ICML, 2007.
- [22]
C. Silpa-Anan and R. Hartley. Optimised KD-trees for fast image descriptor matching. In Proc. CVPR, 2008.
- [23]
A. Vedaldi and S. Soatto. Quick shift and kernel methods for mode seeking. In Proc. ECCV, 2008.
- [24]
A. Vedaldi and A. Zisserman. Efficient additive kernels via explicit feature maps. In Proc. CVPR, 2010.
- [25]
A. Vedaldi and A. Zisserman. Efficient additive kernels via explicit feature maps. PAMI, 34(3), 2012.
- [26]
Z. Wang, B. Fan, and F. Wu. Local intensity order pattern for feature description. In Proc. ICCV, 2011.
- [27]
A. P. Witkin. Scale-space filtering. In Proc. Int. Conf. on Artificial Intelligence, 1983.