Combining text and images for film age appropriateness classification
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Publisher version
Abstract
We combine textual information from a corpus of film scripts and the images of important scenes from IMDB that correspond to these films to create a bimodal dataset (the dataset and scripts can be obtained from https://tinyurl.com/se9tlmr) for film age appropriateness classification with the objective of improving the prediction of age appropriateness for parents and children. We use state-of-the art Deep Learning image feature extraction, including DENSENet, ResNet, Inception, and NASNet. We have tested several Machine learning algorithms and have found xgboost to yield the best results. Previously reported classification accuracy, using only textual features, were 79.1% and 65.3% for American MPAA and British BBFC classification respectively. Using images alone, we achieve 64.8% and 56.7% classification accuracy. The most consistent combination of textual features and images’ features achieves 81.1% and 66.8%, both statistically significant improvements over the use of text only.Citation
Ha, L.A. and Mohamed, E. (2021) Combining text and images for film age appropriateness classification. Procedia Computer Science, 189, pp. 242-249.Publisher
ElsevierJournal
Procedia Computer ScienceAdditional Links
https://www.sciencedirect.com/science/article/pii/S1877050921012060Type
Conference contributionLanguage
enDescription
© 2021 The Authors. Published by Elsevier. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.1016/j.procs.2021.05.087ISSN
1877-0509Scopus Count
Except where otherwise noted, this item's license is described as https://creativecommons.org/licenses/by-nc-nd/4.0/
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