Visual word disambiguation by semantic contexts

Proceedings of the IEEE International Conference on Computer Vision

Volumn , Issue , 2011, Pages 311-318

  Su, Yu ^a   Jurie, Frédéric ^a  

^a UNIVERSITÉ DE CAEN   (France)

Author keywords

[No Author keywords available]

Indexed keywords

BAG OF WORDS; CLASSIFICATION METHODS; DECISION LEVELS; IMAGE DATABASE; IMAGE REPRESENTATIONS; OCCURRENCE PROBABILITY; SEMANTIC CONTEXT; VISUAL WORD;

GRAPHIC METHODS; IMAGE PROCESSING;

SEMANTIC WEB;

EID: 84856687353     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2011.6126257     Document Type: Conference Paper

References (36)

1. A. Bosch, A. Zisserman, and X. Munoz. Scene classification via pLSA. In ECCV, 2006. 6
2. c.-c. Chang and c.-.T. Lin. LTBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2:27:1-27:27, 2011. Software available at http://www.csie.ntu.edu.tw/-cjlin/libsvm. 3,5
3. V. Delaitre, J. Laptev, and J. Sivic. Recognizing human actions in still images: A study of bag-of-features and part-based representations. In BMVC, 2010. 2
4. M. Everingham, L. Van Gool, C. Williams, .T. Winn, and A. Zisserman. The PASCAL Visual Object Classes Challenge 2007 results. http://pascallin . ecs. satan. ac.uk/challenges/VOC/vac2007/. 1,5,7
5. M. Everingham, L. Van Gool, C. Williams, .T. Winn, and A. Zisserman. The PASCAL Visual Object Classes Challenge 2010 results. http://pascallin . ecs. satan. ac.uk/challenges/VOC/vac2010/. I
6. A. Farhadi, T. Endres, D. Hoiem, and D. Forsyth. Describing objects by their attributes. In CVPR, 2009. 2,5
7. C. Galleguillos, A. Rabinovich, and S. Belongie. Object categorization using co-occurrence, location and appearance. In CVPR,2008. 1,2
8. G. Griffin, A. Holub, and P. Perona. Caltech-256 object category dataset. Technical Report 7694, California Institute of Technology, 2007. 1
9. H. Harzallah, F. .Turie, and C. Schmid. Combining efficient object localization and image classification. In ICCV, 2009. 7
10. X. He, R. Zemel, and A. Carreira-Perpinan. Multiscale conditional random fields for image labeling. In CVPR, 2004. 1, 2
11. T. Hotmann. Probabilistic latent semantic analysis. In P roc. of Uncertainty in Artificial Intelligence, 1999. 2, 3
12. S. Ito and S. Kubota. Object classification using heterogeneous co-occurrence features. In ECCV, 2011. I, 2
13. F. Khan, .T. van de Weijer, and M. Vanrell. Top-down color attention for object recognition. In ICCV, 2009. 2
14. C. Lampert, H. Nickisch, and S. Harmeling. Learning to detect unseen object classes by between-class attribute transfer. In CVPR, 2009. 2
15. S. Lazebnik, C. Schmid, and J. Ponce. Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In CVPR, 2006. 2, 5, 7
16. c. Leacock and M. Chodorow. Combining local context and WordNet similarity for word sense identification. WordNet: An electronic lexical database, 49(2):265-283, 1998. I
17. L.-J. Li, C. Wang, Y. Lim, D. Blei, and L. Fei-Fei. Building and using a semantivisual image hierarchy. In CVPR, 2011. 2
18. F. Moosmann, B. Triggs, and F. Jurie. Fast discriminative visual codebooks using randomized clustering forests. In NIPS, 2007. 2
19. N. Morioka and S. Satoh. Building compact local pairwise codebook with joint feature space clustering. In ECCV, 2010. 7
20. A. Rabinovich, A. Vedaldi, C. Galleguillos, E. Wiewiora, and S. Belongie. Objects in context. In ICCV, 2007. 1,2
21. J. Sivic, B. Russell, A. Efros, A. Zisserman, and W. Freeman. Discovering objects and their location in images. In ICCV, 2005. 2
22. T. Sivic and A. Zisserman. Video google: A text retrieval approach to object matching in videos. In ICCV, 2003. 1,2
23. Y. Su, M. Allan, and F. Jurie. Improving object classification using semantic attributes. In BMVC, 2010. 3
24. L. Torresani,M . Szummer,a nd A. Fitzgibbon. Efficient object category recognition using classemes. In ECCV, 2010. 2
25. M. Ullah, S. Parizi, and J. Laptev. Improving bag-of-features action recognition with non-local cues. In BMVC, 2010. 2
26. J. van Gemert, C. Veenman, A. Smeulders, and J. M. Geusebroek. Visual word ambiguity. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(7):1271-1283, 2010. 2
27. J. Vogel and B. Schiele. Semantic modeling of natural scenes for content-based image retrieval. International Journal of Computer Vision, 72(2): 133-157,2007. 2,3
28. G. Wang, D. Hoiem, and D. Forsyth. Learning image similarity from f1ickr groups using stochastic intersection kernel machines. In ICCV, 2009. 2,6
29. T. Winn, A. Criminisi, and T. Minka. Object categorization by learned universal visual dictionary. In ICCV, 2005. 5
30. T. Xiao, J. Hays, K. Ehinger, A. Oliva, and A. Torralba. Sun database: Large-scale scene recognition from abbey to zoo. In CVPR,201O. 1,7
31. J. Yang, Y. Li, Y. Tian, L. Duan, and W. Gao. Group-sensitive multiple kernel learning for object categorization. In ICCV, 2009. 7
32. B. Yao and L. Fei-Fei. Modeling mutual context of object and human pose in human-object interaction activities. In CVPR, 2010. 1
33. D. Yarowsky. Word-sense disambiguation using statistical models of Rogel's categories trained on large corpora. In P roceedings of the 14th conference on Computational linguisticsVolume 2, 1992. 1
34. T. Yuan, Y. Wu, and M. Yang. Discovery of collocation patterns: From visual words to visual phrases. In CVPR, 2007. 2
35. Y. Zhang and T. Chen. Efficient kernels for identifying unbounded-order spatial features. In CVPR, 2009. 5,7
36. X. Zhou, K. Yu, T. Zhang, and T. Huang. Image classification using super-vector coding of local image descriptors. In ECCV, 2010. 7