Recursive compositional models for vision: Description and review of recent work

Journal of Mathematical Imaging and Vision

Volumn 41, Issue 1-2, 2011, Pages 122-146

  Zhu, Long ^a   Chen, Yuanhao ^b   Yuille, Alan ^b,c  

^a POLYTECHNIC UNIVERSITY   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c Korea University   (South Korea)

Author keywords

Computer vision; Image parsing; Image processing; Machine learning; Object detection

Indexed keywords

COMPLEX STRUCTURE; COMPOSITIONAL MODELS; DATA SETS; DESIGN PRINCIPLES; EFFICIENT LEARNING; ELEMENTARY STRUCTURE; IMAGE PARSING; INFERENCE ALGORITHM; LONG RANGE; MACHINE-LEARNING; OBJECT DETECTION; OBJECT MODEL; PROBABILISTIC MODELS; SPATIAL RELATIONSHIPS; STATE OF THE ART; VISUAL STRUCTURE;

COMPUTATIONAL COMPLEXITY; INFERENCE ENGINES;

COMPUTER VISION;

EID: 80051550318     PISSN: 09249907     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10851-011-0282-2     Document Type: Review

References (91)

1. Pearl, J.: Probabilistic Reasoning in Intelligent Systems. Morgan-Kaufmann, San Mateo (1988)
2. Manning, C.D., Schütze, H.: Foundations of Statistical Natural Language Processing. MIT Press, Cambridge (1999)
3. Heckerman, D.: A tutorial on learning with Bayesian networks, pp. 301-354 (1999)
4. Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach, 2nd ed. Prentice Hall, New York (2003)
5. S. Zhu D. Mumford 2006 A stochastic grammar of images 2 4 259 362 1198.68160
6. Grenander, U.: Pattern Synthesis: Lectures in Pattern Theory 1. Springer, New York (1976)
7. Grenander, U.: Pattern Analysis: Lectures in Pattern Theory 2. Springer, New York (1978)
8. Tenenbaum, J., Yuille, A.: IPAM Summer School: The Mathematics of the Mind IPAM, UCLA (2007)
9. Jin, Y., Geman, S.: Context and hierarchy in a probabilistic image model. In: CVPR (2), pp. 2145-2152 (2006) (Pubitemid 44931579)
10. Zhu, L., Yuille, A.L.: A hierarchical compositional system for rapid object detection. In: NIPS (2005)
11. Zhu, L., Chen, Y., Lu, Y., Lin, C., Yuille, A.L.: Max margin and/or graph learning for parsing the human body. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2008)
12. Zhu, L., Chen, Y., Ye, X., Yuille, A.L.: Learning a hierarchical log-linear model for rapid deformable object parsing. In: CVPR (2008)
13. Zhu, L., Chen, Y., Lin, Y., Yuille, A.L.: A hierarchical image model for polynomial-time 2d parsing. In: Advances in Neural Information Processing System (2008)
14. Zhu, L., Lin, C., Huang, H., Chen, Y., Yuille, A.L.: Unsupervised structure learning: Hierarchical recursive composition, suspicious coincidence and competitive exclusion. In: Proceedings of the 10th European Conference on Computer Vision (2008)
15. Zhu, L., Chen, Y., Yuille, A.L.: Learning a hierarchical deformable template for rapid deformable object parsing. In: Transactions on Pattern Analysis and Machine Intelligence (2009)
16. Zhu, L., Chen, Y., Torrabla, A., Freeman, W., Yuille, A.L.: Recursive compositional models with re-usable parts for multi-view multi-object detection and parsing. In: CVPR (2010)
17. Zhu, L., Chen, Y., Yuille, A.L., Freeman, W.: Latent hierarchical structure learning for object detection. In: CVPR (2010)
18. Borenstein, E., Ullman, S.: Class-specific, top-down segmentation. In: ECCV (2), pp. 109-124 (2002)
19. Van Gool, E.M.L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes. Challenge, (2007). (VOC2007) Results. http://www.pascal-network.org/challenges/VOC/voc2007/workshop/index.html
20. Russell, B., Torralba, A., Murphy, K., Freeman, W.T.: Labelme: a database and web-based tool for image annotation. In: IJCV (2008)
21. Mori, G.: Guiding model search using segmentation. In: Proceedings of IEEE International Conference on Computer Vision, pp. 1417-1423 (2005) (Pubitemid 44042345)
22. Magee, D.R., Boyle, R.D.: Detecting lameness using 're-sampling condensation' and 'multi-stream cyclic hidden Markov models'. In: Image and Vision Computing, vol. 20, p. 2002 (2002)
23. H. Li S.-C. Yan L.-Z. Peng 2005 Robust non-frontal face alignment with edge based texture J. Comput. Sci. Technol. 20 6 849 854 10.1007/s11390-005- 0849-8 (Pubitemid 43145595)
24. Shotton, J., Winn, J.M., Rother, C., Criminisi, A.: TextonBoost: Joint appearance, shape and context modeling for multi-class object recognition and segmentation. In: ECCV (1), pp. 1-15 (2006) (Pubitemid 44029764)
25. Z. Tu S.C. Zhu 2002 Image segmentation by data-driven Markov chain Monte Carlo IEEE Trans. Pattern Anal. Mach. Intell. 24 5 657 673 10.1109/34.1000239 (Pubitemid 34550432)
26. Tu, Z., Chen, X., Yuille, A.L., Zhu, S.C.: Image parsing: unifying segmentation, detection, and recognition. In: ICCV, pp. 18-25 (2003)
27. Chen, H., Xu, Z., Liu, Z., Zhu, S.C.: Composite templates for cloth modeling and sketching. In: CVPR (1), pp. 943-950 (2006) (Pubitemid 44931424)
28. Wu, Y., Si, Z., Fleming, C., Zhu, S.: Deformable template as active basis. In: Proceedings of International Conference of Computer Vision (2007)
29. Y. Amit D. Geman X. Fan 2004 A coarse-to-fine strategy for multiclass shape detection IEEE Trans. Pattern Anal. Mach. Intell. 26 12 1606 1621 10.1109/TPAMI.2004.111
30. A.S. Willsky 2002 Multiresolution Markov models for signal and image processing Proc. IEEE 90 8 1396 1458 10.1109/JPROC.2002.800717 (Pubitemid 43785865)
31. G.E. Hinton S. Osindero Y.W. Teh 2006 A fast learning algorithm for deep belief nets Neural Comput. 18 7 1527 1554 2224485 1106.68094 10.1162/neco.2006.18.7.1527 (Pubitemid 44024729)
32. Ranzato, M., Boureau, Y.-L., LeCun, Y.: Sparse feature learning for deep belief networks. In: NIPS (2007)
33. Bengio, Y., Lamblin, P., Popovici, P., Larochelle, H.: Greedy layer-wise training of deep networks. In: Advances in Neural Information Processing Systems (NIPS), vol. 19 (2007)
34. Lee, H., Grosse, R., Ranganath, R., Ng, A.: Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In: International Conference on Machine Learning (ICML) (2009)
35. Riesenhuber, M., Poggio, T.: Cbf: A new framework for object categorization in cortex. In: Biologically Motivated Computer Vision, pp. 1-9 (2000)
36. Serre, T., Wolf, L., Poggio, T.: Object recognition with features inspired by visual cortex. In: CVPR (2), pp. 994-1000 (2005)
37. S. Thorpe M. Fabre-Thorpe 2001 Seeking categories in the brain Science 291 5502 260 263 10.1126/science.1058249 (Pubitemid 32096302)
38. K. Fukushima 1988 Neocognitron: A hierarchical neural network capable of visual pattern recognition Neural Netw. 1 2 119 130 10.1016/0893-6080(88)90014-7 (Pubitemid 18624018)
39. F.V. Jensen S.L. Lauritzen K.G. Olesen 1990 Bayesian updating in causal probabilistic networks by local computations Comput. Stat.Q. 4 269 282 1073446
40. J. Tenenbaum T. Griffiths C. Kemp 2006 Theory-based Bayesian models of inductive learning and reasoning Trends Cogn. Sci. 10 7 309 318 10.1016/j.tics.2006.05.009 (Pubitemid 44094231)
41. Collins, M.: Discriminative training methods for hidden Markov models: theory and experiments with perceptron algorithms. In: EMNLP, pp. 1-8 (2002)
42. Taskar, B., Guestrin, C., Koller, D.: Max-margin Markov networks. In: NIPS (2003)
43. Taskar, B., Klein, D., Collins, M., Koller, D., Manning, C.: Max-margin parsing. In: EMNLP (2004)
44. Altun, Y., Tsochantaridis, I., Hofmann, T.: Hidden Markov support vector machines. In: ICML, pp. 3-10 (2003)
45. Tsochantaridis, I., Hofmann, T., Joachims, T., Altun, Y.: Support vector machine learning for interdependent and structured output spaces. In: ICML (2004)
46. Geman, S., Geman, D.: Stochastic relaxation, Gibbs distribution and Bayesian restoration of images (1984)
47. Blake, A., Zisserman, A.: Visual Reconstruction. MIT Press, Cambridge (1987)
48. Geiger, D., Ladendorf, B., Yuille, A.: Occlusions and binocular stereo (1995)
49. Y. Boykov V. Kolmogorov 2001 An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision IEEE Trans. Pattern Anal. Mach. Intell. 26 359 374 (Pubitemid 33331827)
50. Geiger, D., Yuille, A.: A common framework for image segmentation (1991)
51. Felzenswalb, P., Huttenlocher, D.: Efficient belief propagation for early vision (2004)
52. Viola, P., Jones, M.: Robust real-time object detection. International Journal of Computer Vision (2001)
53. S. Konishi A.L. Yuille J.M. Coughlan S.C. Zhu 2003 Statistical edge detection: Learning and evaluating edge cues IEEE Trans. Pattern Anal. Mach. Intell. 25 57 74 10.1109/TPAMI.2003.1159946
54. S.C. Zhu Y.N. Wu D. Mumford 1997 Minimax entropy principle and its application to texture modeling Neural Comput. 9 8 1627 1660 10.1162/neco.1997.9.8.1627 (Pubitemid 127462299)
55. Kumar, S., Hebert, M.: Discriminative random fields: A discriminative framework for contextual interaction in classification. In: ICCV, pp. 1150-1157 (2003)
56. M.A. Fischler R.A. Elschlager 1973 The representation and matching of pictorial structures IEEE Trans. Comput. 22 1 67 92 10.1109/T-C.1973.223602
57. A.L. Yuille P.W. Hallinan D.S. Cohen 1992 Feature extraction from faces using deformable templates Int. J. Comput. Vis. 8 2 99 111 10.1007/BF00127169
58. T.F. Cootes G.J. Edwards C.J. Taylor 1998 Active appearance models Lecture Notes in Computer Science 1407 484 10.1007/BFb0054760 [Online]. Available: citeseer.ist.psu.edu/cootes98active.html
59. J.M. Coughlan A.L. Yuille C. English D. Snow 2000 Efficient deformable template detection and localization without user initialization Comput. Vis. Image Underst. 78 3 303 319 10.1006/cviu.2000.0842
60. Chui, H., Rangarajan, A.: A new algorithm for non-rigid point matching. In: CVPR, pp. 2044-2051 (2000)
61. S. Belongie J. Malik J. Puzicha 2002 Shape matching and object recognition using shape contexts IEEE Trans. Pattern Anal. Mach. Intell. 24 4 509 522 10.1109/34.993558 (Pubitemid 34448769)
62. P.F. Felzenszwalb D.P. Huttenlocher 2005 Pictorial structures for object recognition Int. J. Comput. Vis. 61 1 55 79 10.1023/B:VISI.0000042934.15159.49
63. Fergus, R., Perona, P., Zisserman, A.: A sparse object category model for efficient learning and exhaustive recognition. In: CVPR (1), pp. 380-387 (2005) (Pubitemid 41275967)
64. Tu, Z., Yuille, A.L.: Shape matching and recognition-using generative models and informative features. In: ECCV (3), pp. 195-209 (2004)
65. He, X., Zemel, R.S., Carreira-Perpiñán, M.Á.: Multiscale conditional random fields for image labeling. In: CVPR (2), pp. 695-702 (2004)
66. Winn, J.M., Jojic, N.: Locus: Learning object classes with unsupervised segmentation. In: ICCV, pp. 756-763 (2005) (Pubitemid 44055031)
67. Kumar, M.P., Torr, P.H.S., Zisserman, A.: Obj cut. In: CVPR (1), pp. 18-25 (2005)
68. Felzenszwalb, P.F., Grishick, R.B., McAllister, D., Ramanan, D.: Object detection with discriminatively trained part based models. In: PAMI (2009)
69. Ahuja, N., Todorovic, S.: Learning the taxonomy and models of categories present in arbitrary image. In: ICCV (2007)
70. Sharon, E., Brandt, A., Basri, R.: Fast multiscale image segmentation. In: CVPR, pp. 1070-1077 (2000)
71. Kokkinos, I., Yuille, A.L.: Hop: Hierarchical object parsing. In: CVPR (2009)
72. Zhu, L., Yuille, A.L.: A hierarchical compositional system for rapid object detection. In: NIPS (2005)
73. Chen, Y., Zhu, L., Lin, C., Yuille, A.L., Zhang, H.: Rapid inference on a novel and/or graph for object detection, segmentation and parsing. In: NIPS (2007)
74. He, C.: Empirical studies of structured learning for deformable object parsing. Master's Thesis, Department of Statistics, UCLA (2008)
75. Wu, S., He, X., Lu, H., Yuille, A.: A unified model of short-rang and long-range motion perception. In: NIPS (2010)
76. Yu, C.-N.J., Joachims, T.: Learning structural SVMs with latent variables. In: International Conference on Machine Learning (ICML) (2009)
77. Yuille, A.L., Rangarajan, A.: The concave-convex procedure (CCCP). In: NIPS, pp. 1033-1040 (2001)
78. Leibe, B., Leonardis, A., Schiele, B.: Combined object categorization and segmentation with an implicit shape model. In: ECCV'04 Workshop on Statistical Learning in Computer Vision, Prague, Czech Republic, 2004, pp. 17-32 (2004)
79. J. Coughlan A.L. Yuille 2002 Bayesian a tree search with expected o(n) node expansions for road tracking Neural Comput. 14 8 1929 1958 1009.62022 10.1162/089976602760128072
80. Ren, X., Fowlkes, C., Malik, J.: Cue integration for figure/ground labeling. In: NIPS (2005)
81. Borenstein, E., Malik, J.: Shape guided object segmentation. In: CVPR (1), pp. 969-976 (2006) (Pubitemid 44931427)
82. Cour, T., Shi, J.: Recognizing objects by piecing together the segmentation puzzle. In: CVPR (2007)
83. Levin, A., Weiss, Y.: Learning to combine bottom-up and top-down segmentation. In: ECCV (4), pp. 581-594 (2006) (Pubitemid 44029660)
84. Srinivasan, P., Shi, J.: Bottom-up recognition and parsing of the human body. In: CVPR (2007)
85. Verbeek, J., Triggs, B.: Region classification with Markov field aspect models. In: CVPR (2007)
86. Tu, Z.: Auto-context and its application to high-level vision tasks. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2008)
87. Desai, C., Ramanan, D., Fowlkes, C.: Discriminative models for multi-class object layout. In: Proceedings of the International Conference on Computer Vision (2009)
88. Chen, Y., Zhu, L., Yuille, A.L.: Active mask hierarchies for object detection. In: Proceedings of the 12th European Conference on Computer Vision (2010)
89. E.L. Allwein R.E. Schapire Y. Singer 2000 Reducing multiclass to binary: a unifying approach for margin classifiers J. Mach. Learn. Res. 1 113 141 1884092 10.1162/15324430152733133 (Pubitemid 33738778)
90. Fidler, S., Leonardis, A.: Towards scalable representations of object categories: learning a hierarchy of parts. In: CVPR (2007)
91. C. Rother V. Kolmogorov A. Blake 2004 "grabcut": interactive foreground extraction using iterated graph cuts ACM Trans. Graph. 23 3 309 314 10.1145/1015706.1015720 (Pubitemid 40163720)