Movie posters classification into genres based on low-level features

Abstract

A person can quickly grasp the genre (drama, comedy, cartoons, etc.) from a movie poster, regardless of visual clutter and the level 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, low-level features based on colors and edges are extracted from poster images and used for poster classification into genres. In this paper, poster classification is modeled as a multilabel classification task, where a single movie may belong to more than one class (genre). To simplify and solve the multilabel problem, two methods for multi-label data transformation are described and evaluated given the classification results obtained by distance ranking, Naïve Bayes and RAKEL. Experiments are conducted on a set of 1500 posters with 6 movie genres. Results provide insights into the properties of the discussed algorithms and features.