-
1
-
-
0003922190
-
-
Wiley, New York, NY, USA
-
Duda R.O., Hart P.E., and Stork D.G. Pattern Classification, second ed. (2001), Wiley, New York, NY, USA
-
(2001)
Pattern Classification, second ed.
-
-
Duda, R.O.1
Hart, P.E.2
Stork, D.G.3
-
2
-
-
0004161991
-
-
Prentice-Hall, Englewood Cliffs, NJ, USA
-
Jain A.K., and Dubes R.C. Algorithms for Clustering Data (1988), Prentice-Hall, Englewood Cliffs, NJ, USA
-
(1988)
Algorithms for Clustering Data
-
-
Jain, A.K.1
Dubes, R.C.2
-
3
-
-
84899013108
-
-
A.Y. Ng, M.I. Jordan, Y. Weiss, On spectral clustering: analysis and an algorithm, in: T.G. Dietterich, S. Becker, Z. Ghahramani (Eds.), Advances in Neural Information Processing Systems, vol. 14, MIT Press, Cambridge, MA, USA, 2002, pp. 849-856.
-
-
-
-
4
-
-
0030646927
-
-
J. Shi, J. Malik, Normalized cuts and image segmentation, in: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Juan, Puerto Rico, 17-19 June 1997, pp. 731-737.
-
-
-
-
6
-
-
85139268336
-
-
Y. Weiss, Segmentation using eigenvectors: a unifying view, in: Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 2, Kerkyra, Greece, 20-27 September 1999, pp. 975-982.
-
-
-
-
7
-
-
0037963197
-
Path-based clustering for grouping of smooth curves and texture segmentation
-
Fischer B., and Buhmann J.M. Path-based clustering for grouping of smooth curves and texture segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 25 4 (2003) 513-518
-
(2003)
IEEE Trans. Pattern Anal. Mach. Intell.
, vol.25
, Issue.4
, pp. 513-518
-
-
Fischer, B.1
Buhmann, J.M.2
-
8
-
-
84899003915
-
-
B. Fischer, V. Roth, J.M. Buhmann, Clustering with the connectivity kernel, in: S. Thrun, L. Saul, B. Schölkopf (Eds.), Advances in Neural Information Processing Systems, vol. 16, MIT Press, Cambridge, MA, USA, 2004.
-
-
-
-
9
-
-
84958625480
-
-
B. Fischer, T. Zöller, J.M. Buhmann, Path based pairwise data clustering with application to texture segmentation, in: M.A.T. Figueiredo, J. Zerubia, A.K. Jain (Eds.), Proceedings of the Third International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, Sophia Antipolis, France, 3-5 September 2001, pp. 235-250.
-
-
-
-
10
-
-
0001033261
-
Robust regression: asymptotics, conjectures, and Monte Carlo
-
Huber P.J. Robust regression: asymptotics, conjectures, and Monte Carlo. Ann. Stat. 1 5 (1973) 799-821
-
(1973)
Ann. Stat.
, vol.1
, Issue.5
, pp. 799-821
-
-
Huber, P.J.1
-
11
-
-
0036565814
-
Mean shift: a robust approach toward feature space analysis
-
Comaniciu D., and Meer P. Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24 5 (2002) 603-619
-
(2002)
IEEE Trans. Pattern Anal. Mach. Intell.
, vol.24
, Issue.5
, pp. 603-619
-
-
Comaniciu, D.1
Meer, P.2
-
13
-
-
51949086172
-
-
O. Chapelle, A. Zien, Semi-supervised classification by low density separation, in: Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics, Barbados, 6-8 January 2005, pp. 57-64.
-
-
-
-
14
-
-
84898995558
-
-
L. Zelnik, P. Perona, Self-tuning spectral clustering, in: Advances in Neural Information Processing Systems, vol. 17, 2005.
-
-
-
-
15
-
-
34548020921
-
-
K. Wagstaff, C. Cardie, Clustering with instance-level constraints, in: Proceedings of the 17th International Conference on Machine Learning, Stanford, CA, USA, 29 June-2 July 2000, pp. 1103-1110.
-
-
-
-
16
-
-
34548018242
-
-
K. Wagstaff, C. Cardie, S. Rogers, S. Schroedl, Constrained k-means clustering with background knowledge, in: Proceedings of the 18th International Conference on Machine Learning, Williamstown, MA, USA, 28 June-1 July 2001, pp. 577-584.
-
-
-
-
17
-
-
34548009037
-
-
D. Klein, S.D. Kamvar, C.D. Manning, From instance-level constraints to space-level constraints: making the most of prior knowledge in data clustering, in: Proceedings of the 19th International Conference on Machine Learning, Sydney, Australia, 8-12 July 2002, pp. 307-314.
-
-
-
-
18
-
-
84879571292
-
-
E.P. Xing, A.Y. Ng, M.I. Jordan, S. Russell, Distance metric learning, with application to clustering with side-information, in: S. Becker, S. Thrun, K. Obermayer (Eds.), Advances in Neural Information Processing Systems, vol. 15, MIT Press, Cambridge, MA, USA, 2003, pp. 505-512.
-
-
-
-
19
-
-
1942517347
-
-
A. Bar-Hillel, T. Hertz, N. Shental, D. Weinshall, Learning distance functions using equivalence relations, in: Proceedings of the 20th International Conference on Machine Learning, Washington, DC, USA, 21-24 August 2003, pp. 11-18.
-
-
-
-
20
-
-
14344262274
-
-
H. Chang, D.Y. Yeung, Locally linear metric adaptation for semi-supervised clustering, in: Proceedings of the 21st International Conference on Machine Learning, Banff, Alberta, Canada, 4-8 July 2004, pp. 153-160.
-
-
-
-
21
-
-
1942420345
-
-
Z. Zhang. Learning metrics via discriminant kernels and multidimensional scaling: toward expected Euclidean representation, in: Proceedings of the 20th International Conference on Machine Learning, Washington, DC, USA, 21-24 August 2003, pp. 872-879.
-
-
-
-
22
-
-
84950632109
-
Objective criteria for the evaluation of clustering methods
-
Rand W.M. Objective criteria for the evaluation of clustering methods. J. Am. Stat. Assoc. 66 (1971) 846-850
-
(1971)
J. Am. Stat. Assoc.
, vol.66
, pp. 846-850
-
-
Rand, W.M.1
-
23
-
-
34548048546
-
-
D.B. Graham, N.M. Allinson, Characterizing virtual eigensignatures for general purpose face recognition, in: H. Wechsler, P.J. Phillips, V. Bruce, F. Fogelman-Soulie, T.S. Huang (Eds.), Face Recognition: From Theory to Applications, NATO ASI Series F, Computer and Systems Sciences, vol. 163, 1998, pp. 446-456.
-
-
-
-
24
-
-
0034850577
-
-
D. Martin, C. Fowlkes, D. Tal, J. Malik, A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics, in: Proceedings of the Eighth IEEE International Conference on Computer Vision, vol. 2, Vancouver, BC, Canada, 7-14 July 2001, pp. 416-423.
-
-
-
-
25
-
-
0034844730
-
-
Y.Y. Boykov, M.P. Jolly, Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images, in: Proceedings of the Eighth IEEE International Conference on Computer Vision, vol. 1, Vancouver, BC, Canada, 7-14 July 2001, pp. 105-112.
-
-
-
-
26
-
-
12844275859
-
Lazy snapping
-
Li Y., Sun J., Tang C.K., and Shum H.Y. Lazy snapping. ACM Trans. Graphics 23 3 (2004) 303-308
-
(2004)
ACM Trans. Graphics
, vol.23
, Issue.3
, pp. 303-308
-
-
Li, Y.1
Sun, J.2
Tang, C.K.3
Shum, H.Y.4
-
27
-
-
0742321103
-
Segmentation given partial grouping constraints
-
Yu S.X., and Shi J. Segmentation given partial grouping constraints. IEEE Trans. Pattern Anal. Mach. Intell. 26 2 (2004) 173-183
-
(2004)
IEEE Trans. Pattern Anal. Mach. Intell.
, vol.26
, Issue.2
, pp. 173-183
-
-
Yu, S.X.1
Shi, J.2
-
28
-
-
0742286179
-
Spectral grouping using the Nyström method
-
Fowlkes C., Belongie S., Chung F., and Malik J. Spectral grouping using the Nyström method. IEEE Trans. Pattern Anal. Mach. Intell. 26 2 (2004) 214-225
-
(2004)
IEEE Trans. Pattern Anal. Mach. Intell.
, vol.26
, Issue.2
, pp. 214-225
-
-
Fowlkes, C.1
Belongie, S.2
Chung, F.3
Malik, J.4
-
29
-
-
33749243237
-
-
M. Ouimet, Y. Bengio, Greedy spectral embedding, in: Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics, Barbados, 6-8 January 2005, pp. 253-260.
-
-
-
-
30
-
-
0041941135
-
-
C. Fowlkes, D. Martin, J. Malik, Learning affinity functions for image segmentation: combining patch-based and gradient-based approaches, in: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, Madison, WI, USA, 18-20 June 2003, pp. 54-61.
-
-
-
-
31
-
-
0004094721
-
-
MIT Press, Cambridge, MA, USA
-
Schölkopf B., and Smola A.J. Learning with Kernels (2002), MIT Press, Cambridge, MA, USA
-
(2002)
Learning with Kernels
-
-
Schölkopf, B.1
Smola, A.J.2
-
32
-
-
11144299132
-
-
J. Ham, D.D. Lee, S. Mika, B. Schölkopf, A kernel view of the dimensionality reduction of manifolds, in: Proceedings of the 21st International Conference on Machine Learning, Banff, Alberta, Canada, 4-8 July 2004, pp. 369-376.
-
-
-
|