Abstract
A person can quickly grasp the movie genre (drama, comedy, cartoons, etc.) from a poster, regardless of short observation time, clutter and variety of details. Bearing this in mind, it can be assumed that simple properties of a movie poster should play a significant role in automated detection of movie genres. Therefore, visual features based on colors and structural cues are extracted from poster images and used for poster classification into genres.
A single movie may belong to more than one genre (class), so the poster classification is a multi-label classification task. To solve the multi-label problem, three different types of classification methods were applied and described in this paper. These are: ML-kNN, RAKEL and Naïve Bayes. ML-kNN and RAKEL methods are directly used on multi-label data. For the Naïve Bayes the task is transformed into multiple single-label classifications. Obtained results are evaluated and compared on a poster dataset using different feature subsets. The dataset contains 6000 posters advertising films classified into 18 genres.
The paper gives insights into the properties of the discussed multi-label classification methods and their ability to determine movie genres from posters using low-level visual features.
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Ivasic-Kos, M., Pobar, M., Ipsic, I. (2015). Automatic Movie Posters Classification into Genres. In: Bogdanova, A., Gjorgjevikj, D. (eds) ICT Innovations 2014. ICT Innovations 2014. Advances in Intelligent Systems and Computing, vol 311. Springer, Cham. https://doi.org/10.1007/978-3-319-09879-1_32
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DOI: https://doi.org/10.1007/978-3-319-09879-1_32
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