A data science approach to movies and film director analysis

Chris May, Lior Shamir

Abstract


The creation of movies involves a careful process of planning, recording, and editing of the visual content. Here we propose a quantitative computer-based analysis of movies to identify similarities that can indicate on influential links between films, genres, or film directors. The method works by first extracting a comprehensive set of numerical image content descriptors from a large number of frames from each movie. Then, the most informative descriptors are selected, and the values of the frames are compared to each other to create a similarity matrix between the movies. The similarity matrix is visualized using a phylogeny to show a network of similarities between movies. Experimental results with a dataset of 104 movies show that the method is able to predict the movie based on a single frame with accuracy of ∼74 percent, and the similarity analysis tends to cluster movies of the same directors or of the same movie series. These results show that computer analysis is able to analyze similarities between movies, providing a quantitative approach to film studies. The automatic association of the movies by directors shows that the film director has stronger influence on the visual outcome than cinematographers or actors.


Keywords


Films; cinema; movies; machine learning; pattern recognition

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DOI: https://doi.org/10.5210/fm.v24i6.9629



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