Abstract
This paper describes a novel approach that automatically refines the image annotations generated by a non-parametric density estimation model. We re-rank these initial annotations following a heuristic algorithm, which uses semantic relatedness measures based on keyword correlation on the Web. Existing approaches that rely on keyword co-occurrence can exhibit limitations, as their performance depend on the quality and coverage provided by the training data. Additionally, WordNet based correlation approaches are not able to cope with words that are not in the thesaurus. We illustrate the effectiveness of our Web-based approach by showing some promising results obtained on two datasets, Corel 5k, and ImageCLEF2009.
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References
Jin, R., Chai, J.Y., Si, L.: Effective automatic image annotation via a coherent language model and active learning. In: Proceedings of the 12th International ACM Conference on Multimedia, pp. 892–899 (2004)
Jin, Y., Khan, L., Wang, L., Awad, M.: Image annotations by combining multiple evidence & WordNet. In: Proceedings of the 13th International ACM Conference on Multimedia, pp. 706–715 (2005)
Liu, J., Li, M., Ma, W.Y., Liu, Q., Lu, H.: An adaptive graph model for automatic image annotation. In: Proceedings of the 8th ACM International Workshop on Multimedia Information Retrieval, pp. 61–70 (2006)
Zhou, X., Wang, M., Zhang, Q., Zhang, J., Shi, B.: Automatic image annotation by an iterative approach: incorporating keyword correlations and region matching. In: Proceedings of the International ACM Conference on Image and Video Retrieval, pp. 25–32 (2007)
Llorente, A., Rüger, S.: Using second order statistics to enhance automated image annotation. In: Boughanem, M., et al. (eds.) ECIR 2009. LNCS, vol. 5478, pp. 570–577. Springer, Heidelberg (2009)
Wang, C., Jing, F., Zhang, L., Zhang, H.J.: Image annotation refinement using random walk with restarts. In: Proceedings of the 14th annual ACM International Conference on Multimedia, pp. 647–650. ACM, New York (2006)
Jin, Y., Wang, L., Khan, L.: Improving image annotations using WordNet. In: Candan, K.S., Celentano, A. (eds.) MIS 2005. LNCS, vol. 3665, pp. 115–130. Springer, Heidelberg (2005)
Liu, J., Wang, B., Li, M., Li, Z., Ma, W., Lu, H., Ma, S.: Dual cross-media relevance model for image annotation. In: Proceedings of the 15th International Conference on Multimedia, pp. 605–614 (2007)
Cilibrasi, R., Vitanyi, P.: The Google similarity distance. IEEE Transactions on Knowledge and Data Engineering 19(3), 370–383 (2007)
Gracia, J., Mena, E.: Web-based measure of semantic relatedness. In: Bailey, J., Maier, D., Schewe, K.-D., Thalheim, B., Wang, X.S. (eds.) WISE 2008. LNCS, vol. 5175, pp. 136–150. Springer, Heidelberg (2008)
Yavlinsky, A., Schofield, E., Rüger, S.: Automated image annotation using global features and robust nonparametric density estimation. In: Proceedings of the International ACM Conference on Image and Video Retrieval, pp. 507–517 (2005)
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Llorente, A., Motta, E., Rüger, S. (2009). Image Annotation Refinement Using Web-Based Keyword Correlation. In: Chua, TS., Kompatsiaris, Y., Mérialdo, B., Haas, W., Thallinger, G., Bailer, W. (eds) Semantic Multimedia. SAMT 2009. Lecture Notes in Computer Science, vol 5887. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10543-2_22
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DOI: https://doi.org/10.1007/978-3-642-10543-2_22
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