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This paper deals with the problem of semantic transcoding of CCTV video footage. A framework is proposed that combines Computer Vision algorithms that extract visual semantics, together with Natural Language Processing that automatically builds the domain ontology from unstructured text annotations. The final aim is a system that will link the visual and(More)
In this paper we explore the distribution of training of self-organised maps (SOM) on grid middleware. We propose a two-level architecture and discuss an experimental methodology comprising ensembles of SOMs distributed over a grid with periodic averaging of weights. The purpose of the experiments is to begin to systematically assess the potential for(More)
In general terms the evaluation of a summary depends on how close it is to the chief points in the source text. This begets the question as to what are the chief points in the source text and how is this information used in itself in identifying the source text. This is crucially important when we discuss automatic evaluation of summaries. So the question(More)
A system for the visualization of large collections of images, facilitated by an automatically constructed visual thesaurus, is reported. A corpus-based method for extraction of terminology and ontology of a specialist domain, scene-of-crime, is outlined. The challenge when capturing information in a crime scene is how to later visualise the scene, when all(More)
Statistical pattern recognition techniques, supervised and unsupervised classification techniques being two good examples here, rely on the computations of similarity and distance metrics. The distances are computed in a multi-dimensional space. The axes of this space in principle relate to the features inherent in the input data. Usually such features are(More)
'Integrated' classification refers to the conjunctive or competitive use of two or more (neural) classifiers. A cooperative neural network system comprising two independently trained Kohonen networks and cooperating with the help of a Hebbian network, is described. The effectiveness of such a network is demonstrated by using it to retrieve images and(More)
Automatic text categorization requires the construction of appropriate surrogates for documents within a text collection. The surrogates, often called document vectors, are used to train learning systems for categorising unseen documents. A comparison of different measures (tfidf and weirdness) for creating document vectors is presented together with two(More)