Refining image annotation using contextual relations between words

Proceedings of the 6th ACM International Conference on Image and Video Retrieval, CIVR 2007

Volumn , Issue , 2007, Pages 425-432

  Wang, Yong ^a   Gong, Shaogang ^a  

^a QUEEN MARY UNIVERSITY OF LONDON   (United Kingdom)

Author keywords

Image annotation; Normalized Google distance; Semantic relation

Indexed keywords

RELATIONAL DATABASE SYSTEMS; SEMANTIC WEB; VECTOR QUANTIZATION;

IMAGE ANNOTATION; KERNEL BASED PROBABILISTIC CLASSIFIER; NORMALIZED GOOGLE DISTANCE; RELEVANCE VECTOR MACHINE (RVM); SEMANTIC RELATION;

IMAGE ANALYSIS;

EID: 36849068461     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1282280.1282343     Document Type: Conference Paper

References (19)

1. D. M. Blei and M. I. Jordan. Modeling annotated data. In Proceedings of ACM SIGIR, pages 127-134, 2003.
2. E. Chang, K. Goh, G. Sychay, and G. Wu. CBSA: content-based soft annotation for multimodal image retrieval using Bayes point machines. CSVT, IEEE Transactions on, pages 26-38, Janary 2003.
3. Chris Dance et al. Visual categorization with bags of keypoints. In ECCV International Workshop on Statistical Learning in Computer Vision, 2004.
4. R. Cilibrasi and P. Vitanyi. The google similarity distance. Knowledge and Data Engineering, IEEE/ACM Transactions on. To appear, also available at http://cilibrar.com/.
5. G. Carneiro, and N. Vasconcelos. Formulating semantic image annotation as a supervised learning problem. In Proceedings of IEEE CVPR, June 2005.
6. Y. Gao and J. Fan. Incorporating concept ontology to enable probabilistic concept reasoning for multi-level image annotation. In Proceedings of A CM Workshop on MIR, pages 79-88, 2006.
7. J. Jeon et al. Automatic image annotation and retrieval using cross-media relevance models. In Proceedings of ACM SIGIR, pages 119-126, 2003.
8. J. Lafferty, A. McCallum and F. Pereira. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In Proceedings of ICML, 2001.
9. Y. Jin, L. Khan, L. Wang, and M. Awad. Image annotations by combining multiple evidence & wordNet. In Proceedings of ACM MULTIMEDIA, pages 706-715, 2005.
10. Kobus Barnard et al. Matching words and pictures. J. Mach. Learn. Res., 3:1107-1135, 2003.
11. J. Li and J. Z. Wang. Automatic linguistic indexing of pictures by a statistical modeling approach. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 25(9):1075-1088, 2003.
12. D. G. Lowe. Distinctive image features from scale-invariant keypoints. IJCV, 60(2):91-110, 2004.
13. G. A. Miller. Wordnet: a lexical database for english. Commun. ACM, 38(11):39-41, 1995.
14. B. . P. M. . J. Y. . . S. L. Feng et al, TITLE = Multiple bernoulli relevance models for image and video annotation.
15. M. Srikanth, J. Varner, M. Bowden, and D. Moldovan. Exploiting ontologies for automatic image annotation. In Proceedings of SIGIR, pages 552-558, 2005.
16. M. E. Tipping. Sparse bayesian learning and the relevance vector machine. J. Mach. Learn. Res., 1:211-244, 2001.
17. V. Lavrenko, R. Manmatha and J. Jeon. A model for learning the semantics of pictures. In Proceedings of NIPS, 2003.
18. C. Wang, F. Jing, L. Zhang, and H.-J. Zhang. Image annotation refinement using random walk with restarts. In Proceedings of ACM MULTIMEDIA, pages 647-650, 2006.
19. Y. Wu, E. Y. Chang, and B. L. Tseng. Multimodal metadata fusion using causal strength. In Proceedings of ACM MULTIMEDIA, pages 872-881, 2005.