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We propose a novel algorithm for extracting information by mining the feature space clusters and then assigning salient concepts to them. Bayesian techniques for extracting concepts from multimedia usually suffer either from lack of data or from too complex concepts to be represented by a single statistical model. An incremental information extraction(More)
The multimedia content delivery chain poses today many challenges. The increasing terminal diversity, network heterogeneity and the pressure to satisfy the user preferences are raising the need for content to be customized in order to provide the user the best possible experience. This paper addresses the problem of multimedia customization by (1)(More)
In this paper we describe the geographic information retrieval system developed and results achieved by the Multimedia & Information Systems team for GeoCLEF 2006. We detail our methods for generating and applying co-occurrence models for the purpose of place name disambiguation, our use of named entity recognition tools and text indexing applications. The(More)
This paper details an integrated methodology to optimise Knowledge reuse and sharing, illustrated with a use case in the aeronautics domain. It uses Ontologies as a central modelling strategy for the Capture of Knowledge from legacy documents via automated means, or directly in systems interfacing with Knowledge workers, via user-defined, web-based forms.(More)
We address the problem of searching multimedia by semantic similarity in a keyword space. In contrast to previous research we represent multimedia content by a vector of keywords instead of a vector of low-level features. This vector of keywords can be obtained through user manual annotations or computed by an automatic annotation algorithm. In this(More)
This paper is about automatically annotating images with keywords in order to be able to retrieve images with text searches. Our approach is to model keywords such as 'mountain' and 'city' in terms of visual features that were extracted from images. In contrast to other algorithms, each specific keyword model considers not only its own training data but(More)
This paper describes the participation of the NovaSearch group at TREC Clinical Decision Support 2014. As this is the first edition of the track, we decided to assess the performance of multiple information retrieval techniques: retrieval functions, re-ranking, query expansion and classification of medical articles into question categories. The best(More)