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On the influence of low-level visual features in film classification

BACKGROUND: In this paper we present a model of parameters to aesthetically characterize films using a multi-disciplinary approach: by combining film theory, visual low-level video descriptors (modeled in order to supply aesthetic information) and classification techniques using machine and deep lea...

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Detalles Bibliográficos
Autores principales: Álvarez, Federico, Sánchez, Faustino, Hernández-Peñaloza, Gustavo, Jiménez, David, Menéndez, José Manuel, Cisneros, Guillermo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6386315/
https://www.ncbi.nlm.nih.gov/pubmed/30794549
http://dx.doi.org/10.1371/journal.pone.0211406
Descripción
Sumario:BACKGROUND: In this paper we present a model of parameters to aesthetically characterize films using a multi-disciplinary approach: by combining film theory, visual low-level video descriptors (modeled in order to supply aesthetic information) and classification techniques using machine and deep learning. METHODS: Four different tests have been developed, each for a different application, proving the model's usefulness. These applications are: aesthetic style clustering, prediction of production year, genre detection and influence on film popularity. RESULTS: The results are compared against high-level information to determine the accuracy of the model to classify films without knowing such information previously. The main difference with other film characterization approaches is that we are able to isolate the influence of high-level descriptors to really understand the relevance of low-level features and, accordingly propose a useful set of low-level visual descriptors for that purpose. This model has been tested with a representative number of films to prove that it can be used for different applications.