KMi Publications

External Publications

15 publications | pharos


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Llorente, A. (2010) Semantics and Statistics for Automated Image Annotation PhD Thesis

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Llorente, A., Motta, E. and Rueger, S. (2010) Exploring the Semantics Behind a Collection to Improve Automated Image Annotation, Proceedings of the 10th Workshop of the Cross-Language Evaluation Forum (CLEF 2009), LCNS 6242, Part II, Springer

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Llorente, A., Manmatha, R. and Rueger, S. (2010) Image Retrieval using Markov Random Fields and Global Image Features, Proceedings of the ACM International Conference on Image and Video Retrieval, Xi'an, China, pp. 243-250

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Nowak, S., Llorente, A., Motta, E. and Rueger, S. (2010) The Effect of Semantic Relatedness Measures on Multi-label Classification Evaluation, Proceedings of the ACM International Conference on Image and Video Retrieval, Xi'an, China, pp. 303-310

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Llorente, A., Motta, E. and Rueger, S. (2009) Image Annotation Refinement using Web-based Keyword Correlation, Proceedings of the 4th International Conference on Semantic and Digital Media Technologies, Graz, Austria, 5887, pp. 188--191

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Liu, H., Zagorac, S., Uren, V., Song, D. and Rueger, S. (2009) Enabling Effective User Interactions in Content-Based Image Retrieval, The 5th Asia Information Retrieval Symposium (AIRS2009), Sapporo, Japan, pp. 265-276, Springer

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Little, S. and Rueger, S. (2009) Conservation of Effort in Feature Selection for Image Annotation, Workshop: IEEE Multimedia Signals Processing (MMSP), Brazil

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Llorente, A., Little, S. and Rueger, S. (2009) MMIS at ImageCLEF 2009: Non-parametric Density Estimation Algorithms, Working notes for the CLEF 2009 Workshop, Corfu, Greece

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Llorente, A. and Rueger, S. (2009) Using Second Order Statistics to Enhance Automated Image Annotation, Proceedings of the 31st European Conference on Information Retrieval, Toulouse, France, 5478, pp. 570-577

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Llorente, A. and Rueger, S. (2008) Can a Probabilistic Image Annotation System be Improved Using a Co-occurrence Approach?, Proceedings of the Workshop on Cross-Media Information Analysis, Extraction and Management, Koblenz, Germany, 437, pp. 33-42

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Llorente, A., Zagorac, S., Little, S., Hu, R., Kumar, A., Shaik, S., Ma, X. and Rueger, S. (2008) Semantic Video Annotation using Background Knowledge and Similarity-based Video Retrieval, TREC Video Retrieval Evaluation Notebook Papers, Gaithersburg, Maryland, NIST

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Llorente, A., Overell, S., Liu, H., Hu, R., Rae, A., Zhu, J., Song, D. and Rueger, S. (2008) Exploiting Term Co-occurrence for Enhancing Automated Image Annotation, Proceedings of the 9th Workshop of the Cross-Language Evaluation Forum (CLEF 2008), LNCS 5706, pp. 632--639, Springer

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Overell, S., Llorente, A., Liu, H., Hu, R., Rae, A., Zhu, J., Song, D. and Rueger, S. (2008) MMIS at ImageCLEF 2008: Experiments combining Different Evidence Sources, Working notes for the CLEF 2008 Workshop, Aarhus, Denmark

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Liu, H., Song, D., Rueger, S., Hu, R. and Uren, V. (2008) Comparing Dissimilarity Measures for Content-based Image Retrieval, The 4th Asia Information Retrieval Symposium (AIRS2008), Harbin, China, pp. 44-50, Springer

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Huang, Z., Shen, H., Zhou, X., Song, D. and Rueger, S. (2007) Dimensionality Reduction for Dimension-specific Search, Poster at The 30th Annual International ACM SIGIR Conference (SIGIR'2007), pp. 849-850

 
 
 

Multimedia and Information Systems is...


Multimedia and Information Systems
Our research is centred around the theme of Multimedia Information Retrieval, ie, Video Search Engines, Image Databases, Spoken Document Retrieval, Music Retrieval, Query Languages and Query Mediation.

We focus on content-based information retrieval over a wide range of data spanning form unstructured text and unlabelled images over spoken documents and music to videos. This encompasses the modelling of human perception of relevance and similarity, the learning from user actions and the up-to-date presentation of information. Currently we are building a research version of an integrated multimedia information retrieval system MIR to be used as a research prototype. We aim for a system that understands the user's information need and successfully links it to the appropriate information sources, be it a report or a TV news clip. This work is guided by the vision that an automated knowledge extraction system ultimately empowers people making efficient use of information sources without the burden of filing data into specialised databases.

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