Abstract
In the current context of monetization of multimedia content, it is common to see the appearance of edited replicas of popular videos to take advantage of the momentum of those. In this work, several parameters of near-duplicate video detection systems based on codebooks are studied using techniques from the field of information retrieval. As a result, a system with high average precision, usually higher than 85%, is obtained. Several hyperparameters of the system, such as the aggregation mechanisms and the retrieval model, are analyzed, thus adjusting the system for optimal performance.
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One of the authors (G.H.) gratefully acknowledges the Junta de Castilla y León and the European Regional Development Fund for financial support.
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Hernández, G., Arrieta, A.G., Novais, P., Rodríguez, S. (2022). Codebook-Based Near-Duplicate Video Detection. In: Sanjurjo González, H., Pastor López, I., García Bringas, P., Quintián, H., Corchado, E. (eds) 16th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2021). SOCO 2021. Advances in Intelligent Systems and Computing, vol 1401. Springer, Cham. https://doi.org/10.1007/978-3-030-87869-6_27
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