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

Date Published: February 22, 2019

Publisher: Public Library of Science

Author(s): Federico Álvarez, Faustino Sánchez, Gustavo Hernández-Peñaloza, David Jiménez, José Manuel Menéndez, Guillermo Cisneros, Mathieu Hatt.

http://doi.org/10.1371/journal.pone.0211406

Abstract

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.

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.

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.

Partial Text

Aesthetic film analysis has traditionally been an academic activity belonging to cinema studies, performed without using automatic tools. Nowadays manual tools such as Cinemetrics [1] provide an important help to film scholars, allowing for the extraction of metrics to compare styles from different films, filmmakers or aesthetic movements. However, key elements as shot changes have to be manually set, making it a tedious process, as well as inaccurate by the loss of meaningful information not perceptible by the human visual system.

Our model uses low-level descriptors to automatically characterize films according to aesthetic criteria. It also allows us to predict high-level aesthetic features and to classify films according to visual style (which is normally done by analysts). It provides other functionalities, such as the prediction of production year.

Some classification tests have been developed to validate our aesthetic film model. We present two kinds of tests: the first kind of tests is an unsupervised classification, which we use to validate the correct clustering of films according to their style (“aesthetic style clustering”), as we have defined in section “Related work”; the second kind of tests is a supervised classification, which we use to predict high-level features: production year and genre tests, which are theoretically independent from our descriptors and a last test was done to assess the influence of the descriptors model in the film popularity. The unsupervised classification test is focused on the precision and recall of the aesthetic style classification, and the purpose of the supervised tests is to check the possibilities of our approach in order to predict quantifiable high-level features, which are related, in an indirect way, to the aesthetic of films.

Film aesthetic modeling had not yet been considered to create real applications related to video recommendation, Decision Support Systems (DSS), or any other predictive application. It had only been taken into account manually by film analyzers, but there were no models to implement automatic real systems. The existing automatic systems only use other kind of information, related to features such as director, actors, nationality or collaborative techniques.

 

Source:

http://doi.org/10.1371/journal.pone.0211406

 

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