Baselines for image annotation

International Journal of Computer Vision

Volumn 90, Issue 1, 2010, Pages 88-105

  Makadia, Ameesh ^a   Pavlovic, Vladimir ^b   Kumar, Sanjiv ^a  

^a GOOGLE INC   (United States)

^b RUTGERS UNIVERSITY   (United States)

Author keywords

Baseline techniques; Image annotation; Label transfer; Nearest neighbor

Indexed keywords

BASELINE METHODS; BASELINE TECHNIQUES; COMPLEX MODEL; DATA SETS; DISTANCE MEASURE; IMAGE ANNOTATION; IMAGE DATA; LOW-LEVEL IMAGE FEATURES; NEAREST NEIGHBORS; STATE-OF-THE-ART METHODS; TRANSFER MECHANISMS;

LABELS;

IMAGE ANALYSIS;

EID: 77954086656     PISSN: 09205691     EISSN: 15731405     Source Type: Journal    
DOI: 10.1007/s11263-010-0338-6     Document Type: Article

References (29)

1. K. Barnard M. Johnson 2005 Word sense disambiguation with pictures Artificial Intelligence 167 13 30 10.1016/j.artint.2005.04.009 (Pubitemid 41240676)
2. Blei, D. M., & Jordan, M. I. (2003a). Modeling annotated data. In Proceedings of the ACM SIGIR (pp. 127-134).
3. D. Blei A. Ng M. Jordan 2003 Latent Dirichlet allocation Journal of Machine Learning Research 3 993 1022 1112.68379 10.1162/jmlr.2003.3.4-5.993
4. Carneiro, G., & Vasconcelos, N. (2005a). A database centric view of semantic image annotation and retrieval. In SIGIR (pp. 559-566).
5. Carneiro, G., & Vasconcelos, N. (2005b). Formulating semantic image annotation as a supervised learning problem. In IEEE CVPR (pp. 559-566).
6. Carneiro, G., Chan, A. B., Moreno, P. J., & Vasconcelos, N. (2007). Supervised learning of semantic classes for image annotation and retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(3).
7. Datta, R., Joshi, D., Li, J., & Wang, J. Z. (2008). Image retrieval: ideas, influences, and trends of the new age. ACM Computing Surveys.
8. Duygulu, P., Barnard, K., de Freitas, J. F. G., & Forsyth, D. A. (2002). Object recognition as machine translation: learning a lexicon for a fixed image vocabulary'. In ECCV (pp. 97-112).
9. Feng, S. L., Manmatha, R., & Lavrenko, V. (2004). Multiple Bernoulli relevance models for image and video annotation. In IEEE conference on computer vision and pattern recognition.
10. Frome, A., Singer, Y., Sha, F., & Malik, J. (2007). Learning globally-consistent local distance functions for shape-based image retrieval and classification. In Proceedings of the IEEE international conference on computer vision, Rio de Janeiro, Brazil.
11. Gao, Y., & Fan, J. (2006). Incorporating concept ontology to enable probabilistic concept reasoning for multi-level image annotation. In Proceedings of the 8th ACM international workshop on multimedia information retrieval (pp. 79-88).
12. Gionis, A., Indyk, P., & Motwani, R. (1999). Similarity search in high dimensions via hashing. In VLDB '99: proceedings of the 25th international conference on very large data bases (pp. 518-529), San Francisco, CA, USA.
13. Hare, J. S., Lewisa, P. H., Enserb, P. G. B., & Sandomb, C. J. (2006). Mind the gap: another look at the problem of the semantic gap in image retrieval. Multimedia Content, Analysis, Management and Retrieval.
14. Jeon, J., Lavrenko, V., & Manmatha, R. (2003). Automatic image annotation and retrieval using cross-media relevance models. In Proceedings of the ACM SIGIR conference on research and development in information retrieval. New York, NY, USA (pp. 119-126).
15. Jin, R., Chai, J. Y., & Si, L. (2004). Effective automatic image annotation via a coherent language model and active learning. In Proceedings of the ACM multimedia conference (pp. 892-899).
16. Lavrenko, V., Manmatha, R., & Jeon, J. (2004). A model for learning the semantics of pictures. In Advances in neural information processing systems (Vol. 16).
17. Li, J., & Wang, J. (2003). Automatic linguistic indexing of pictures by a statistical modeling approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25.
18. Li, J., & Wang, J. Z. (2006). Real-time computerized annotation of pictures. In Proceedings of the ACM multimedia (pp. 911-920).
19. Makadia, A., Pavlovic, V., & Kumar, S. (2008). A new baseline for image annotation. In ECCV.
20. Metzler, D., & Manmatha, R. (2005). An inference network approach to image retrieval. In Image and video retrieval (pp. 42-50).
21. Monay, F., & Gatica-Perez, D. (2003). On image auto-annotation with latent space models. In Proceedings of the ACM international conference on multimedia (pp. 275-278).
22. Mori, Y., Takahashi, H., & Oka, R. (1999). Image-to-word transformation based on dividing and vector quantizing images with words. In Proceedings of the first international workshop on multimedia intelligent storage and retrieval management (MISRM).
23. J. Shi J. Malik 2000 Normalized cuts and image segmentation IEEE Transactions on Pattern Analysis and Machine Intelligence 22 8 888 905 10.1109/34.868688
24. R. Tibshirani 1996 Regression shrinkage and selection via the Lasso Journal of the Royal Statistical Society B 58 1 267 288 0850.62538 1379242
25. Varma, M., & Ray, D. (2007). Learning the discriminative power-invariance trade-off. In Proceedings of the IEEE international conference on computer vision, Rio de Janeiro, Brazil.
26. von Ahn, L., & Dabbish, L. (2004). Labeling images with a computer game. In ACM CHI.
27. Wang, L., Liu, L., & Khan, L. (2004). Automatic image annotation and retrieval using subspace clustering algorithm. In ACM international workshop on multimedia databases.
28. Yang, C., Dong, M., & Hua, J. (2006). Region-based image annotation using asymmetrical support vector machine-based multiple-instance learning. In Proceedings of the IEEE international conference on computer vision and pattern recognition.
29. Yavlinsky, A., Schofield, E., & Ruger, S. (2005). Automated image annotation using global features and robust nonparametric density estimation. In CIVR.