-
2
-
-
14344264451
-
Integrating constraints and metric learning in semi-supervised clustering
-
M. Bilenko, S. Basu, and R. Mooney. Integrating constraints and metric learning in semi-supervised clustering. In International Conference on Machine Learning, volume 1, pages 81-88, 2004.
-
(2004)
International Conference on Machine Learning
, vol.1
, pp. 81-88
-
-
Bilenko, M.1
Basu, S.2
Mooney, R.3
-
4
-
-
33845577819
-
Acceleration strategies for gaussian mean-shift image segmentation
-
IEEE
-
M. Carreira-Perpinan. Acceleration strategies for gaussian mean-shift image segmentation. In Computer Vision and Pattern Recognition, volume 1, pages 1160-1167. IEEE, 2006.
-
(2006)
Computer Vision and Pattern Recognition
, vol.1
, pp. 1160-1167
-
-
Carreira-Perpinan, M.1
-
6
-
-
34047257042
-
Gaussian mean-shift is an em algorithm
-
M. Carreira-Perpinan. Gaussian mean-shift is an em algorithm. IEEE TPAMI, 29(5):767-776, 2007.
-
(2007)
IEEE TPAMI
, vol.29
, Issue.5
, pp. 767-776
-
-
Carreira-Perpinan, M.1
-
7
-
-
0029357425
-
Mean shift, mode seeking, and clustering
-
Y. Cheng. Mean shift, mode seeking, and clustering. IEEE TPAMI, 17(7):790-799, 1995.
-
(1995)
IEEE TPAMI
, vol.17
, Issue.7
, pp. 790-799
-
-
Cheng, Y.1
-
8
-
-
0036565814
-
Mean shift: A robust approach toward feature space analysis
-
603-è19, May
-
D. Comaniciu and P. Meer. Mean shift: A robust approach toward feature space analysis. IEEE TPAMI, 24(5):603-è19, May 2002.
-
(2002)
IEEE TPAMI
, vol.24
, Issue.5
-
-
Comaniciu, D.1
Meer, P.2
-
9
-
-
84880095768
-
Clustering with constraints: Feasibility issues and the k-means algorithm
-
SIAM
-
I. Davidson and S. Ravi. Clustering with constraints: Feasibility issues and the k-means algorithm. In International Conference on Data Mining. SIAM, 2005.
-
(2005)
International Conference on Data Mining
-
-
Davidson, I.1
Ravi, S.2
-
11
-
-
15044364880
-
Mean shift is a bound optimization
-
Mar
-
M. Fashing and C. Tomasi. Mean shift is a bound optimization. IEEE TPAMI, 27:471-474, Mar. 2005.
-
(2005)
IEEE TPAMI
, vol.27
, pp. 471-474
-
-
Fashing, M.1
Tomasi, C.2
-
12
-
-
0016421071
-
The estimation of the gradient of a density function, with application in pattern recognition
-
K. Fukunaga and L. Hostetler. The estimation of the gradient of a density function, with application in pattern recognition. IEEE Transactions on Information Theory, 21:32-40, 1975.
-
(1975)
IEEE Transactions on Information Theory
, vol.21
, pp. 32-40
-
-
Fukunaga, K.1
Hostetler, L.2
-
14
-
-
0345414073
-
Mean shift based clustering in high dimensions: A texture classification example
-
IEEE
-
B. Georgescu, I. Shimshoni, and P. Meer. Mean shift based clustering in high dimensions: a texture classification example. In International Conference on Computer Vision, volume 1, pages 456-463. IEEE, 2003.
-
(2003)
International Conference on Computer Vision
, vol.1
, pp. 456-463
-
-
Georgescu, B.1
Shimshoni, I.2
Meer, P.3
-
15
-
-
58049095501
-
Face recognition using laplacianfaces
-
Mar
-
X. He, S. Yan, Y. Hu, P. Niyogi, and H. Zhang. Face recognition using laplacianfaces. IEEE TPAMI, 27(3):1-13, Mar. 2005.
-
(2005)
IEEE TPAMI
, vol.27
, Issue.3
, pp. 1-13
-
-
He, X.1
Yan, S.2
Hu, Y.3
Niyogi, P.4
Zhang, H.5
-
16
-
-
9444294778
-
From instance-level constrains to space-level constraints: Making the most of prior knowledge in data clustering
-
D. Klein, S. Kamvar, and C. Manning. From instance-level constrains to space-level constraints: Making the most of prior knowledge in data clustering. In International Conference on Machine Learning, pages 307-314, 2002.
-
(2002)
International Conference on Machine Learning
, pp. 307-314
-
-
Klein, D.1
Kamvar, S.2
Manning, C.3
-
18
-
-
0042377235
-
Constrained k-means clustering with background knowledge
-
K. Wagstaff, C. Cardie, S. Rogers, and S. Schroedl. Constrained k-means clustering with background knowledge. In International Conference on Machine Learning, pages 577-584, 2001.
-
(2001)
International Conference on Machine Learning
, pp. 577-584
-
-
Wagstaff, K.1
Cardie, C.2
Rogers, S.3
Schroedl, S.4
-
20
-
-
78651528447
-
Fast mean shift with accurate and stable convergence
-
P. Wang, D. Lee, A. Gray, and J. Rehg. Fast mean shift with accurate and stable convergence. In International Conference on Artificial Intelligence and Statistics, volume 2, pages 604-611, 2007.
-
(2007)
International Conference on Artificial Intelligence and Statistics
, vol.2
, pp. 604-611
-
-
Wang, P.1
Lee, D.2
Gray, A.3
Rehg, J.4
-
21
-
-
0344551865
-
Improved fast gauss transform and efficient kernel density estimation
-
IEEE
-
C. Yang, R. Duraiswami, N. A. Gumerov, and L. Davis. Improved fast gauss transform and efficient kernel density estimation. In International Conference on Computer Vision, volume 1, pages 664-671. IEEE, 2003.
-
(2003)
International Conference on Computer Vision
, vol.1
, pp. 664-671
-
-
Yang, C.1
Duraiswami, R.2
Gumerov, N.A.3
Davis, L.4
-
22
-
-
50649092224
-
Half quadratic analysis for mean shift: With extension to a sequential data mode-seeking method
-
IEEE
-
X. T. Yuan and S. Z. Li. Half quadratic analysis for mean shift: with extension to a sequential data mode-seeking method. In International Conference on Computer Vision. IEEE, 2007.
-
(2007)
International Conference on Computer Vision
-
-
Yuan, X.T.1
Li, S.Z.2
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