CRF 图结构扩展 参考文献汇总

具有扩展图结构的CRF 模型
动态CRF 模型[81, 146-150]
[81] C. Sutton and A. McCallum, Composition of conditional random fields for transfer learning,
Proceedings of the conference on Human Language Technology and Empirical Methods in
Natural Language Processing 2005:748–754.
[146] A. McCallum, K. Rohanimanesh, and C. Sutton, Dynamic conditional random fields for
jointly labeling multiple sequences, Neural Information Processing System Workshop on
Syntax, Semantics and Statistics, 2003.
[147] C. Sutton, K. Rohanimanesh, and A. McCallum, Dynamic conditional random fields:
factorized probabilistic models for labeling and segmenting sequence data, International
Conference on Machine Learning, Banff, Canada, 2004:282-289.
[148] M. Shimosaka, T. Mori, and T. Sato, Robust action recognition and segmentation with
multi-task conditional random fields, IEEE International Conference on Robotics and
Automation, Roma, Italy, 2007:3780-3786.
[149] L. Wang and D. Suter, Recognizing human activities from silhouettes: motion subspace and
factorial discriminative graphical model, IEEE Conference on Computer Vision and Pattern
Recognition, 2007.
[150] T.-y. Wu, C.-c. Lian, and J. Y.-j. Hsu, Joint recognition of multiple concurrent activities
using factorial conditional random fields, AAAI Workshop on Plan, Activity, and Intent
Recognition, 2007.

隐CRF 模型[47, 152-168] y->h,h->x,y->x;
[47] Y.-Y. Wang, A. Acero, M. Mahajan, and J. Lee, Combining statistical and knowledge-based
spoken language understanding in conditional models, Proceedings of the COLING/ACL on
Main conference poster sessions 2006:882 - 889.
[152] Y.-H. Sung, C. Boulis, C. Manning, and D. Jurafsky, Regularization, adaptation, and
non-Independent features improve hidden conditional random fields for phone classification,
IEEE Workshop on Automatic Speech Recognition and Understanding 2007:347-352.
[153] M. Szummer, Learning diagram parts with hidden random fields, International Conference
on Document Analysis and Recognition 2005:1188 - 1193.
[154] A. Quattoni, M. Collins, and T. Darrell, Conditional random fields for object recognition,
Neural Information Processing Systems, 2004.
[155] M. Welling and C. Sutton, Learning in Markov random fields with contrastive free energies,
International Workshop on Artificial Intelligence and Statistics, Barbados, 2005:397-404.
[156] S. Reiter, B. o. Schuller, and G. Rigoll, Hidden conditional random fields for meeting
segmentation, IEEE International Conference on Multimedia and Expo, 2007:639-642.
[157] S. B. Wang, A. Quattoni, L.-P. Morency, D. Demirdjian, and T. Darrell, Hidden conditional
random fields for gesture recognition, IEEE Conference on Computer Vision and Pattern
Recognition, 2006:1521 - 1527.
[158] M. Mahajan, A. Gunawardana, and A. Acero, Training algorithms for hidden conditional
random fields, International Conference on Acoustics, Speech, and Signal Processing, 2006.
[159] C. Yi-ping, Y

. Xiu-zi, Q. Jiang, Z. Yin, and Z. San-yuan. Adaptive foreground and shadow
segmentation using hidden conditional random fields. Journal of Zhejiang University
SCIENCE A, 2007, 8 (4):586-592.
[160] J. Winn and J. Shotton, The layout consistent random field for recognizing and segmenting
国防科学技术大学研究生院博士学位论文
第157 页
partially occluded objects, IEEE Conference on Computer Vision and Pattern Recognition
2006:37-44.
[161] D. Hoiem, C. Rother, and J. Winn, 3D layoutCRF for multi-view object class recognition
and segmentation, IEEE Conference on Computer Vision and Pattern Recognition,
2007:1-8.
[162] L.-P. Morency, A. Quattoni, and T. Darrell, Latent-dynamic discriminative models for
continuous gesture recognition, IEEE Conference on Computer Vision and Pattern
Recognition, 2007.
[163] A. Quattoni, M. Collins, and T. Darrell, Incorporating semantic constraints into a
discriminative categorization and labelling model, IEEE International Conference on
Computer Vision, 2005:1877- 1877.
[164] S. Kumar and M. Hebert, A hierarchical field framework for unified context-based
classification, IEEE International Conference on Computer Vision, 2005:1284 - 1291.
[165] A. Kapoor and J. Winn. Located hidden random fields: learning discriminative parts for
object detection. Lecture Notes in Computer Science, 2006, 3953:302-315.
[166] Y.-H. Sung, C. Boulis, and D. Jurafsky, Maximum conditional likelihood linear regression
and maximum a posteriori for hidden conditional random fields speaker adaptation, IEEE
International Conference on Acoustics, Speech and Signal Processing, 2008:4293-4296.
[167] A. Quattoni, S. Wang, L.-P. Morency, M. Collins, and T. Dar. Hidden conditional random
fields. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29
(10):1848-1853.
[168] 褚一平，张引，叶修梓，张三元. 基于隐条件随机场的自适应视频分割算法. 自动化学
报, 2007, 33 (12):1252-1258.


[154, 167, 169]：y->h,h->x(图2.8 b)


[153, 162] y->h,hi->xi(图2.8 c)


布局一致随机场(Layout Consistent CRF, LCCRF)[160, 161]（对一些具有特定物理意义的隐变量增加限制条件，也可以得到一些特别的HCRF模型。如在目标识别中，考虑隐变量为目标的组成成分，当对这些隐变量增加其在目标中所处位置的限制条件）


定位HCRF(Located HCRF，LHCR)[165]模型(增加更多的隐变量层也可以进一步扩展HCRF，如在目标识别中，在具有表示目标组成成分的隐变量层的前题下，进一步增加用于表征位置的隐变量层，便得到定位LHCRF)


树结构CRF 模型[170-174]
[170] T. Cohn and P. Blunsom, Semantic role labelling with tree conditional random fields,
Proceedings CoNLL-2005: Ninth Conference on Computational Natural Language, 2005.
[171] J. Tang, M. Hong, J. Li, and B. Liang. Tree-structured Conditional Random Fields for
Semantic Annotation. Lecture Note

s in Computer Science, 2006, 4273:640 -653.
[172] D. Ramanan and C. Sminchisescu, Training deformable models for localization, IEEE
Computer Society Conference on Computer Vision and Pattern Recognition, 2006:206- 213.
[173] P. Awasthi, A. Gagrani, and B. Ravindran, Image modeling using tree structured conditional
random fields, International Joint Conferences on Artificial Intelligence, 2007:2060-2065.
[174] J. Reynolds and K. Murphy, Figure-ground segmentation using a hierarchical conditional
random field, Canadian Conference on Computer and Robot Vision 2007: 175-182.

混合CRF 模型 [145, 175]
[145] X. He, R. S. Zemel, and D. Ray. Learning and incorporating top-down cues in image
segmentation. Lecture Notes in Computer Science, 2006, 3951:338-351.
[175] C. Sutton, M. Sindelar, and A. McCallum, Reducing weight undertraining in structured
discriminative learning, Conference on Human Language Technology and North American
Association for Computational Linguistics (HLT-NAACL), 2006:89 - 95.


半-Markov CRF[176-179]，分割CRF[180]，2 维CRF[181]，作者相关CRF[182]
[176] T. M. T. Do and T. Artières, Conditional random field for tracking user behavior based on
his eye's movements, NIPS 2005 Workshop on Machine Learning for Implicit Feedback and
User Modeling, 2005.
[177] I. R. Mansuri and S. Sarawagi, Integrating unstructured data into relational databases,
International Conference on Data Engineering 2006.
[178] D. Okanohara, Y. Miyao, Y. Tsuruoka, and J. i. Tsujii, Improving the scalability of
semi-markov conditional random fields for named entity recognition, Proceedings of the
21st International Conference on Computational Linguistics and 44th Annual Meeting of the
ACL, 2006:465–472.
[179] S. Sarawagi and W. Cohen, Semi-markov conditional random fields for information
extraction, Neural Information Processing Systems, 2004.


[180] Y. Liu, J. Carbonell, V. Gopalakrishnan, and P. Weigele, Protein quaternary fold recognition
using conditional graphical models, IJCAI, 2007:937-945.

[181] J. Zhu, Z. Nie, J.-R. Wen, B. Zhang, and W.-Y. Ma, 2D conditional random fields for web
information extraction, International Conference on Machine Learning, Bonn, Germany,
2005:1044-1051.
[182] Y. Mao and G. Lebanon, Isotonic conditional random fields and local sentiment flow,
Neural Information Processing Systems, 2006:961-968.