Daniel Beauchêne

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This paper addresses the automatic movie genre classification in the specific case of animated movies. Two types of information are used. The first one are movie synopsis. For each genre, a symbolic representation of a thematic intensity is extracted from synopsis. Addressed visually, movie content is described with symbolic representations of different(More)
Investigations into the classification of meat samples as either fresh or frozen-then-thawed have been performed using near infrared spectroscopy and two chemometric techniques: factorial discriminant analysis and SIMCA. Work was performed on meat pieces using a fibre optic probe for spectral acquisition. A sample of meat (m. longissimus dorsi) from each of(More)
Multimedia documents are increasingly numerous. Their efficient management requires tools to provide services that measure up to users' expectations, based on the contents of these voluminous document databases. This implies a number of challenges. Although we can extract highly symbolic concepts from texts, a wide semantic gap appears when processing(More)
In the context of animated movie characterization, we present an information fusion approach mixing very different types of data related to the activity within a movie. These data are the features extracted from images, words extracted from the synopses and expert knowledge. The difficulty of this fusion is due to the very different semantic level of these(More)
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