Spatial distance dependent Chinese restaurant processes for image segmentation

Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011

Volumn , Issue , 2011, Pages

  Ghosh, Soumya ^a   Ungureanu, Andrei B ^b   Sudderth, Erik B ^a   Blei, David M ^c  

^a Brown University   (United States)

^b MORGAN STANLEY   (United States)

^c Princeton University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NETWORKS;

DATAPOINTS; EXTERNAL SENSE; HUMAN LIKE; HYPER-PARAMETER; IMAGES SEGMENTATIONS; NATURAL IMAGE SEGMENTATIONS; PROCESS-MODELS; RANDOM PARTITIONS; SPATIAL DISTANCE; UNSUPERVISED IMAGE SEGMENTATION;

IMAGE SEGMENTATION;

EID: 85162542038     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (22)

1. D. M. Blei and P. I. Frazier. Distant dependent chinese restaurant processes. Journal of Machine Learning Research, 12:2461-2488, August 2011.
2. J. Pitman. Combinatorial Stochastic Processes. Lecture Notes for St. Flour Summer School. Springer-Verlag, New York, NY, 2002.
3. S. Geman and D. Geman. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on pattern analysis andmachine intelligence, 6(6):721-741, November 1984.
4. Richard Socher, Andrew Maas, and Christopher D. Manning. Spectral chinese restaurant processes: Nonparametric clustering based on similarities. In Fourteenth InternationalConference on Artificial Intelligence and Statistics (AISTATS), 2011.
5. Y. W. Teh, M. I. Jordan, M. J. Beal, and D. M. Blei. Hierarchical Dirichlet processes. Journal of American Statistical Association, 25(2):1566-1581, 2006.
6. D. Comaniciu and P.Meer. Mean shift: A robust approach toward feature space analysis. IEEE Transactions on pattern analysis and machine intelligence, pages 603-619, 2002.
7. C. Rother, V. Kolmogorov, and A. Blake. Grabcut: Interactive foreground extraction using iterated graph cuts. In ACM Transactions on Graphics (TOG), volume 23, pages 309-314, 2004.
8. J. Shi and J. Malik. Normalized cuts and image segmentation. IEEE Trans. PAMI, 22(8):888-905, 2000.
9. C. Fowlkes, D. Martin, and J. Malik. Learning affinity functions for image segmentation: Combining patch-based and gradient-based approaches. CVPR, 2:54-61, 2003.
10. E. B. Sudderth and M. I. Jordan. Shared segmentation of natural scenes using dependent pitman-yor processes. NIPS 22, 2008.
11. P. Orbanz and J. M. Buhmann. Smooth image segmentation by nonparametric Bayesian inference. In ECCV, volume 1, pages 444-457, 2006.
12. Lan Du, Lu Ren, David Dunson, and Lawrence Carin. A bayesian model for simultaneous image clustering, annotation and object segmentation. In NIPS 22, pages 486-494. 2009.
13. J. Pitman and M. Yor. The two-parameter Poisson-Dirichlet distribution derived from a stable subordinator. Annals of Probability, 25(2):855-900, 1997.
14. J. A. Duan, M. Guindani, and A. E. Gelfand. Generalized spatial Dirichlet process models. Biometrika, 94(4):809-825, 2007.
15. X. Ren and J. Malik. Learning a classification model for segmentation. ICCV, 2003.
16. C. Carson, S. Belongie, H. Greenspan, and J. Malik. Blobworld: Image segmentation using expectation-maximization and its application to image querying. PAMI, 24(8):1026-1038, August 2002.
17. B. C. Russell, A. Torralba, K. P. Murphy, andW. T. Freeman. Labelme: A database web-based tool for image annotation. IJCV, 77:157-173, 2008.
18. C. Robert and G. Casella. Monte Carlo Statistical Methods. Springer Texts in Statistics. Springer-Verlag, New York, NY, 2004.
19. A. Oliva and A. Torralba. Modeling the shape of the scene: A holistic representation of the spatial envelope. IJCV, 42(3):145-175, 2001.
20. G. Mori. Guiding model search using segmentation. ICCV, 2005.
21. D. R. Martin, C.C. Fowlkes, and J. Malik. Learning to detect natural image boundaries using local brightness, color, and texture cues. IEEE Trans. PAMI, 26(5):530-549, 2004.
22. W.M. Rand. Objective criteria for the evaluation of clustering methods. Journal of the AmericanStatistical Association, pages 846-850, 1971.