Abstract
Multimedia content annotation is a key issue in the current convergence of audiovisual entertainment and information media. In this context, automatic genre classification (AGC) provides a simple and effective solution to describe video contents in a structured and well understandable way. In this paper a method for classifying the genre of TV broadcasted programmes is presented. In our approach, we consider four groups of features, which include both low-level visual descriptors and higher level semantic information. For each type of these features we derive a characteristic vector and use it as input data of a multilayer perceptron (MLP). Then, we use a linear combination of the outputs of the four MLPs to perform genre classification of TV programmes. The experimental results on more than 100 hours of broadcasted material showed the effectiveness of our approach, achieving a classification accuracy of ~92%.
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Montagnuolo, M., Messina, A. (2007). TV Genre Classification Using Multimodal Information and Multilayer Perceptrons. In: Basili, R., Pazienza, M.T. (eds) AI*IA 2007: Artificial Intelligence and Human-Oriented Computing. AI*IA 2007. Lecture Notes in Computer Science(), vol 4733. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74782-6_63
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DOI: https://doi.org/10.1007/978-3-540-74782-6_63
Publisher Name: Springer, Berlin, Heidelberg
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