Abstract
Automatic image annotation has attracted much attention recently, due to its wide applicability (such as image retrieval by semantics). Most of the known statistical model-based annotation methods learn the joint distribution of the keywords and the image blobs decomposed by segmentation or gride approaches. The effects of these methods suffer from the sparseness of the image blobs. As a result, the estimated joint distribution is need to be “smoothed”. In this paper, we present a topic-based smoothing method to overcome the sparseness problems, and integrated with a general image annotation model. Experimental results on 5,000 images demonstrate that our method can achieves significant improvement in annotation effectiveness over an existing method.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Barnard, K., Duygulu, P., Forsyth, D.: Clustering Art. In: Proceedings of IEEE ICPR (2001)
Barnard, K., Duygulu, P., de Freitas, N., Forsyth, D., Blei, D., Jordan, M.I.: Matching words and pictures. Journal of Machine Learning Research 3, 1107–1135 (2003)
Benitez, A., Chang, S.-.F.: Semantic knowledge construction from annotated image collections. In: Proc. IEEE ICME, Lausanne (July 2002)
Blei, D., Jordan, M.I.: Modeling annotated data. In: Proc. of the 26th Intl. ACM SIGIR Conf., pp. 127–134 (2003)
Cusano, C., Ciocca, G., Schettini, R.: Image Annotation Using Svm. In: Proceedings of Internet imaging IV. SPIE, vol. 5304 (2004)
Duygulu, P., Barnard, K., de Freitas, N., Forsyth, D.: Object recognition as machine translation: Learning a lexicon for a fixed image vocabulary. In: 7th European Conference on Computer Vision, pp. IV: 97–112 (2002)
Jeon, J., Lavrenko, V., Manmatha, R.: Automatic Image Annotation and Retrieval using Cross-Media Relevance Models. In: Proc.of the 26th ACM SIGIR conference, pp. 119–126 (2003)
Jin, R., Chai, J., Si, L.: Effective Automatic Image Annotation Via A Coherent Language Model and Active Learning. In: ACM MM 2004 (2004)
Monay, F., Gatica-PerezOn, D.: On image auto-annotation with latent space models. In: Proc. of the ACM Int’l. Conf. on Multimedia (2003)
Mori, Y., Takahashi, H., Oka, R.: Image-to-word transformation based on dividing and vector quantizing images with words. In: First Int’l. Workshop on Multimedia Intelligent Storage and Retrieval Management (1999)
Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8), 888–905 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhou, X., Ye, J., Chen, L., Zhang, L., Shi, B. (2005). Automatic Image Annotation Based on Topic-Based Smoothing. In: Gallagher, M., Hogan, J.P., Maire, F. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2005. IDEAL 2005. Lecture Notes in Computer Science, vol 3578. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11508069_12
Download citation
DOI: https://doi.org/10.1007/11508069_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26972-4
Online ISBN: 978-3-540-31693-0
eBook Packages: Computer ScienceComputer Science (R0)