Abstract
Multimedia data collections immersed into social networks may be explored from the point of view of varying documents and users characteristics. In this paper, we develop a unified model to embed documents, concepts and users into coherent structures from which to extract optimal subsets and to diffuse information. The result is the definition information propagation strategies and of active guiding navigation strategies of both the user and document networks, as a complement to classical search operations. Example benefits brought by our model are provided via experimental results.
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.
Similar content being viewed by others
References
Bartholomew, D.J., Knott, M.: Latent variable models and factor analysis. Oxford University Press, Inc., New York (1999)
Cord, M., Gosselin, P.H.: Image retrieval using long-term semantic learning. In: IEEE International Conference on Image Processing (2006)
Craver, S., Yeo, B.-L., Yeung, M.: Multi-linearisation data structure for image browsing. In: SPIE Conf. on Storage and Retrieval for Image and Video DBs VII (1999)
Deerwester, S., Dumais, S., Landauer, T., Furnas, G., Harshman, R.: Indexing by latent semantic analysis. Journal of the American Society of Information Science 4, 391–407 (1990)
Gaussier, E., Goutte, C.: Relation between plsa and nmf and implications. In: SIGIR 2005: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 601–602. ACM, New York (2005)
He, X., King, O., Ma, W.-Y., Li, M., Zhang, H.-J.: Learning a semantic space from user’s relevance feedback for image retrieval. IEEE Transactions on Circuits and Systems for Video Technology 13(1), 39–48 (2003)
Hofmann, T.: Latent semantic models for collaborative filtering. ACM Transactions on Information Systems (TOIS) 22(1), 89 (2004)
Kabán, A., Girolami, M.A.: Fast extraction of semantic features from a latent semantic indexed text corpus. Neural Process. Lett. 15(1), 31–43 (2002)
Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 401(6755), 788–791 (1999)
Marchand-Maillet, S., Bruno, É.: Collection Guiding: A new framework for handling large multimediacollections. In: Audio-visual Content And Information Visualization In Digital Librairies, Cortona, Italy (2005)
Morrison, D., Bruno, E., Marchand-Maillet, S.: Capturing the semantics of user interaction: A review and case study. In: Emergent Web Intelligence. Springer, Heidelberg (2010)
Morrison, D., Marchand-Maillet, S., Bruno, E.: Semantic clustering of images using patterns of relevance feedback. In: Proceedings of the 6th International Workshop on Content-based Multimedia Indexing, London, UK, June 18-20 (2008)
Morrison, D., Marchand-Maillet, S., Bruno, E.: Modelling long-term relevance feedback. In: Proceedings of the ECIR Workshop on Information Retrieval over Social Networks, Toulouse, FR, April 6 (2009)
Müller, H., Müller, W., Squire, D.M., Marchand-Maillet, S., Pun, T.: Long-term learning from user behavior in content-based image retrieval. Technical report, Université de Genève (2000)
Nguyen, G.P., Worring, M.: Optimization of interactive visual similarity based search. ACM TOMCCAP 4(1) (2008)
Rubner, Y.: Perceptual Metrics for Image Database Navigation. PhD thesis, Stanford University (1999)
Szekely, E., Bruno, E., Marchand-Maillet, S.: High dimensional multimodal embedding for cluster preservation. Technical Report VGTR:0801, Viper - University of Geneva (2008)
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
Marchand-Maillet, S., Morrison, D., Szekely, E., Kludas, J., Vonwyl, M., Bruno, E. (2011). Mining Networked Media Collections. In: Detyniecki, M., GarcÃa-Serrano, A., Nürnberger, A. (eds) Adaptive Multimedia Retrieval. Understanding Media and Adapting to the User. AMR 2009. Lecture Notes in Computer Science, vol 6535. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18449-9_1
Download citation
DOI: https://doi.org/10.1007/978-3-642-18449-9_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-18448-2
Online ISBN: 978-3-642-18449-9
eBook Packages: Computer ScienceComputer Science (R0)