Summary
At present, there are vast amounts of digital media available on the web. In the Web image retrieval, the semantics of an image is a big problem, generally, the search engines index the text associated to the image of Web pages. This text doesn’t correspond really to them.
The image annotation is an effective technology for improving the Web image retrieval. Indeed, it permits assigning semantics to an image, by attributing to the images keywords corresponding to the senses conveyed by these images. To improve the automatic image annotation (AIA), a strategy consists in correlating the textual and visual information of the images. In this work, we propose an image auto-annotation system based on AnnotB-LSA algorithm that integrates the LSA model.
The main focus of this paper is two-fold. First, in the training stage, we perform clustering of regions into classes of similar visual regions called blobs according to their visual feature. This clustering prepares a visual space by learning from the annotated images corpus and permits to annotate the blobs by performing the algorithm annotB-LSA. Second, in the new image annotation stage, we can annotate a new image by selecting the key words of the blobs to which its regions belong. Experiment results show that our proposed system is performing.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Monay, F., Gatica-Perez, D.: One Picture Auto Annotation with Latent Space Models. In: Proc. ACM Int. Conf. one Multimedia (ACM MESSRS), Berkeley, California, USA (2003)
Monay, F., Gatica-Perez, D.: PLSA-based Auto-Annotation Picture: Constraining the Latent Space. In: Proc. ACM Int. Conf. one Multimedia (ACM MESSRS), NewYork, USA (2004)
Tollari, S.: Indexing and Research of pictures by Fusion of Textual and Visual information. Thesis of doctorate. University of the south Toulan-Var (2006)
Weber, R., Schek, H.-J., Blott, S.: In Quantitative Analysis and Performance Study heart Similarity-Search Methods in High-Dimensional Space. In: Proceedings of International Conference of Very Large Dated Bases (VLDB), pp. 194–205 (1998)
Zhao, Y., Zhao, Y., Zhu, Z., Flap, S.: TO Novel Picture Annotation Design Based one Neural Network. In: Eighth International Conference one Intelligent Systems Design and Applications. IEEE, Los Alamitos (2008), 978-0-7695-3382-7/08, doi:10.1109/ISDA.55
Khan, L.: Standards heart Picture Annotation using Semantic Web. To compute Standards & Interfaces 29, 169–204 (2007)
Xiao, Y., Chua, T.-S., Lee, C.-H.: Fusion of region and image-based techniques for automatic image annotation. In: Cham, T.-J., Cai, J., Dorai, C., Rajan, D., Chua, T.-S., Chia, L.-T. (eds.) MMM 2007. LNCS, vol. 4351, pp. 247–258. Springer, Heidelberg (2007)
Wang, X.-J., Zhang, L., Jing, F., There, W.-Y.: My. AnnoSearch: Auto-Annotation picture Search by. In: Proceedings of the 2006 IEEES Computer Society Conference one to Compute Vision and Pattern Recognition, June 17-22 (2006)
Lu, J., Zhao, T., Zhang, Y.: Feature Based Selection One Genetic Algorithm heart Picture Annotation. Knowledge-Based Systems (2008), 0950-7051
Jin, W., Shi, R., Chua, S.T.: In Semi-Naive Bayesian Method Incorporating Clustering with Even-Wise Constraints heart Auto Picture Annotation. In: MM 2004, October 10-16. ACM, New York (2004)
Jeon, J., Lavrenko, V., Manmatha, R.: Automatic Image Annotation and Retrieval using Cross-Media Relevance Models. In: SIGIR 2003. ACM Press, Toronto (2003), 1-58113-646-3/03/0007
Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by Latent Semantic Analysis. Newspaper of the American Society of Information Science 41(6), 391–407 (1990)
Barnard, K., Duygulu, P., Freitas, N., Forsyth, D., Blei, D., Jordan, I.: Matching words and pictures. Newspaper of Plots Learning Research 3, 1107–1135 (2003)
Shi, J., Malik, J.: Normalized Cuts and Picture Segmentation. IEEE Transactions one pattern Analysis and Plots Intelligence 22(8), 888–905 (2000)
Liu, W., Tang, X.: Learning an Image-Word Embedding for Image Auto-Annotation on the Nonlinear Latent Space. In: MM 2005, November 6-11. ACM, Singapore (2005), 1-59593-044-2/05/0011
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bakalem, M., Benblidia, N., Ait-Aoudia, S. (2011). A Novel Image Auto-annotation Based on Blobs Annotation. In: Choraś, R.S. (eds) Image Processing and Communications Challenges 3. Advances in Intelligent and Soft Computing, vol 102. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23154-4_13
Download citation
DOI: https://doi.org/10.1007/978-3-642-23154-4_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23153-7
Online ISBN: 978-3-642-23154-4
eBook Packages: EngineeringEngineering (R0)