Open AccessOn the influence of low-level visual features in film classification
Author(s) -
Federico Álvarez,
Faustino Sánchez,
Gustavo Hernández-Peñaloza,
David Jiménez,
José Manuel Menéndez,
Guillermo Cisneros
Publication year - 2019
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0211406
Subject(s) - computer science , cluster analysis , artificial intelligence , relevance (law) , popularity , set (abstract data type) , pattern recognition (psychology) , machine learning , information retrieval , natural language processing , psychology , social psychology , political science , law , programming language
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.