Probabilistic joint image segmentation and labeling by figure-ground composition

International Journal of Computer Vision

Volumn 107, Issue 1, 2014, Pages 40-57

  Ion, Adrian ^a   Carreira, João ^b   Sminchisescu, Cristian ^c,d  

^a VIENNA UNIVERSITY OF TECHNOLOGY   (Austria)

^b UNIVERSITY OF COIMBRA   (Portugal)

^c LUND UNIVERSITY   (Sweden)

^d INSTITUTE OF MATHEMATICS   (Romania)

Author keywords

Image labeling; Image segmentation; Learning and categorization; Semantic segmentation; Statistical models

Indexed keywords

EID: 84894475814     PISSN: 09205691     EISSN: 15731405     Source Type: Journal    
DOI: 10.1007/s11263-013-0663-7     Document Type: Article

References (58)

1. Arbelaez, P.; Maire, M.; Fowlkes, C.; Malik, J. (2009). From contours to regions: An empirical evaluation. In: IEEE International Conference on Computer Vision and Pattern Recognition.
2. Arbelaez, P.; Hariharan, B.; Gu, C.; Gupta, S.; Bourdev, L. D.; Malik, J. (2012). Semantic segmentation using regions and parts. In: IEEE International Conference on Computer Vision and Pattern Recognition.
3. Bagon, S.; Boiman, O.; Irani, M. (2008). What is a good image segment? a unified approach to segment extraction. In: European Conference on Computer Vision.
4. Barbu, A.; Zhu, S. C. (2005). Generalizing swendsen-wang to sampling arbitrary posterior probabilities. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(8), 1239-1253.
5. Barnard, K.; Duygulu, P.; Forsyth, D.; de Freitas, N.; Blei, D. M.; Jordan, M. (2003). Matching words and pictures. Journal of Machine Learning Research, 3, 1107-1135.
6. Bomze, I.; Budinich, M.; Pardalos, P.; Pelillo, M. (1999). Handbook of combinatorial optimization (pp. 1-74). Dordrecht: Kluwer Academic.
7. Bomze, I.; Pelillo, M.; Stix, V. (2000). Approximating the maximum weight clique using replicator dynamics. IEEE Transactions on Neural Networks, 11(6), 1228-1241.
8. Brendel, W.; Todorovic, S. (2010). Segmentation as maximum-weight independent set. In: Advances in Neural Information Processing Systems.
9. Carreira, J.; Sminchisescu, C. (2012). Cpmc: Automatic object segmentation using constrained parametric min-cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(7), 1312-1328.
10. Carreira, J.; Caseiro, R.; Batista, J.; Sminchisescu, C. (2012a). Semantic segmentation with second-order pooling. In: European Conference on Computer Vision.
11. Carreira, J.; Li, F.; Sminchisescu, C. (2012b). Object recognition by sequential figure-ground ranking. International Journal of Computer Vision, 98(3), 243-262.
12. Comaniciu, D.; Meer, P. (2002). Mean shift: A robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(5), 603-619.
13. Cour, T.; Gogin, N.; Shi, J. (2005). Learning spectral graph segmentation. In: Artificial Intelligence and Statistics.
14. Csurka, G.; Perronnin, F. (2010). An efficient approach to semantic segmentation. International Journal of Computer Vision, 88, 1-15.
15. Dann, C.; Gehler, P. V.; Roth, S.; Nowozin, S. (2012). Pottics - the potts topic model for semantic image segmentation. In: Proceedings of DAGM/OAGM Symposium.
16. Endres, I.; Hoiem, D. (2010). Category independent object proposals. In: European Conference on Computer Vision.
17. Everingham, M.; Van Gool, L.; Williams, C. K. I.; Winn, J.; Zisserman, A. (2010). The pascal visual object classes (voc) challenge. International Journal of Computer Vision, 88(2), 303-338.
18. Everingham, M.; Van Gool, L.; Williams, C.K.I.; Winn, J. & Zisserman, A. (2012). The PASCAL visual object classes challenge (VOC) results. http://www.pascal-network.org/challenges/VOC/.
19. Farabet, C.; Couprie, C.; Najman, L.; LeCun, Y. (2013). Learning hierarchical features for scene labeling. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35, 1915-1929.
20. Felzenszwalb, P.; Huttenlocher, D. (2004). Efficient graph-based image segmentation. International Journal of Computer Vision, 59(2), 167-181.
21. Felzenszwalb, P. F.; Girshick, R. B.; McAllester, D.; Ramanan, D. (2010). Object detection with discriminatively trained part-based models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(9), 1627-1645.
22. Fulkerson, B.; Vedaldi, A.; Soatto, S. (2009). Class segmentation and object localization with superpixel neighborhoods. In: IEEE International Conference on Computer Vision.
23. Ghose, T.; Palmer, S. (2005). Surface convexity and extremal edges in depth and figure-ground perception. Journal of Vision, 5(8), 970-970.
24. Gonfaus, J. M.; Boix, X.; van de Weijer, J.; Bagdanov, A. D.; Serrat, J.; Gonzalez, J. (2010). Harmony potentials for joint classification and segmentation. In: IEEE International Conference on Computer Vision and Pattern Recognition.
25. Gould, S.; Rodgers, J.; Cohen, D.; Elidan, G.; Koller, D. (2008). Multi-class segmentation with relative location prior. International Journal of Computer Vision, 80(3), 300-316.
26. Gould, S.; Fulton, R.; Koller, D. (2009a). Decomposing a scene into geometric and semantically consistent regions. In: IEEE International Conference on Computer Vision.
27. Gould, S.; Gao, T.; Koller, D. (2009b). Region-based segmentation and object detection. In: Advances in Neural Information Processing Systems.
28. He, X.; Zemel, R. S.; Carreira-Perpinan, M. (2004). Multiscale conditional random fields for image labeling. In: IEEE International Conference on Computer Vision and Pattern Recognition.
29. Hinton, G. E. (2002). Training products of experts by minimizing contrastive divergence. Neural Computation, 14(8), 1771-1800.
30. Hoiem, D.; Efros, A.; Hebert, M. (2007). Recovering surface layout from an image. International Journal of Computer Vision, 75(1), 151-172.
31. Huggins, P.; Chen, H.; Belhumeur, P.; Zucker, S. (2001). Finding folds: On the appearance and identification of occlusion. In: IEEE International Conference on Computer Vision and Pattern Recognition.
32. Ion, A.; Carreira, J.; Sminchisescu, C. (2011a). Image segmentation by figure-ground composition into maximal cliques. In: IEEE International Conference on Computer Vision.
33. Ion, A.; Carreira, J.; Sminchisescu, C. (2011b). Probabilistic joint image segmentation and labeling. In: Advances in Neural Information Processing Systems.
34. Kohli, P.; Ladicky, L.; Torr, P. (2008). Robust higher order potentials for enforcing label consistency. In: IEEE International Conference on Computer Vision and Pattern Recognition.
35. Kolmogorov, V. (2006). Convergent tree-reweighted message passing for energy minimization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(10), 1568-1583.
36. Kumar, M. P.; Koller, D. (2010). Efficiently selecting regions for scene understanding. In: IEEE International Conference on Computer Vision and Pattern Recognition.
37. Kumar, S.; August, J.; Hebert, M. (2005). Exploiting inference for approximate parameter learning in discriminative fields: An empirical study. In: Energy Minimization Methods in Computer Vision and Pattern Recognition.
38. Ladicky, L.; Russell, C.; Kohli, P.; Torr, P. H. S. (2009). Associative hierarchical crfs for object class image segmentation. In: IEEE International Conference on Computer Vision.
39. Ladicky, L.; Sturgess, P.; Alaharia, K.; Russel, C.; Torr, P. (2010). What, where & how many ? combining object detectors and crfs. In: European Conference on Computer Vision.
40. Leichter, I. & Lindenbaum, M.; (2009). Boundary ownership by lifting to 2.1d. In: IEEE International Conference on Computer Vision.
41. Li, F.; Ionescu, C.; Sminchisescu, C. (2010). Random Fourier approximations for skewed multiplicative histogram kernel. In: Proceedings of DAGM Symposium.
42. Lim, J.; Arbelaez, P.; Gu, C.; Malik, J. (2009). Context by region ancestry. In: IEEE International Conference on Computer Vision.
43. Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91- 110.
44. Malisiewicz, T.; Efros, A. (2007). Improving spatial support for objects via multiple segmentations. In: British Machine Vision Conference.
45. Martin, D.; Fowlkes, C.; Tal, D.; Malik, J. (2001). A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: IEEE International Conference on Computer Vision.
46. Nowozin, S.; Gehler, P.; Lampert, C. (2010). On parameter learning in crf-based approaches to object class image segmentation. In: European Conference on Computer Vision.
47. Pantofaru, C.; Schmid, C.; Hebert, M. (2008). Object recognition by integrating multiple image segmentations. In: European Conference on Computer Vision.
48. Rahimi, A.; Recht, B. (2007). Random features for large-scale kernel machines. In: Advances in Neural Information Processing Systems.
49. Ren, X.; Malik, J. (2003). Learning a classification model for segmentation. In: IEEE International Conference on Computer Vision.
50. Ren, X.; Fowlkes, C.; Malik, J. (2006). Figure/ground assignment in natural images. In: European Conference on Computer Vision.
51. van de Sande, K. E. A.; Gevers, T.; Snoek, C. G. M. (2010). Evaluating color descriptors for object and scene recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(9), 1582-1596.
52. Sarawagi, S.; Cohen, W. W. (2004). Semi-markov conditional random fields for information extraction. In: Advances in Neural Information Processing Systems.
53. Sharon, E.; Galun, M.; Sharon, D.; Basri, R.; Brandt, A. (2006). Hierarchy and adaptivity in segmenting visual scenes. Nature, 442(7104), 719-846.
54. Shi, J.; Malik, J. (2000). Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8), 888-905.
55. Shotton, J.; Winn, J.; Rother, C.; Criminisi, A. (2009). Textonboost for image understanding: Multi-class object recognition and segmentation by jointly modeling texture, layout, and context. International Journal of Computer Vision, 81, 2-23.
56. Tu, Z.; Chen, X.; Yuille, A.; Zhu, S. C. (2003). Image parsing: unifying segmentation, detection, and recognition. In: IEEE International Conference on Computer Vision.
57. Xia, W.; Song, Z.; Feng, J.; Cheong, L.F. & Yan, S. (2012). Segmentation over detection by coupled global and local sparse representations. In: European Conference on Computer Vision.
58. Yang, Y.; Hallman, S.; Ramanan, D.; Fowlkes, C. C. (2012). Layered object models for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(9), 1731-1743.