Demystifying Visual Features of Movie Posters for Multilabel Genre Identification

In the film industry, movie posters have been an essential part of advertising and marketing for many decades and continue to play a vital role even today in the form of digital posters through online, social media, and over-the-top (OTT) platforms. Typically, movie posters can effectively promote and communicate the essence of a film, such as its genre, visual style/tone, vibe, and storyline cue/theme, which are essential to attract potential viewers. Identifying the genres of a movie often has significant practical applications in recommending the film to target audiences. Previous studies on genre identification have primarily focused on sources such as plot synopses, subtitles, metadata, movie scenes, and trailer videos; however, posters precede the availability of these sources and provide prerelease implicit information to generate mass interest. In this article, we work for automated multilabel movie genre identification only from poster images, without any aid of additional textual/metadata/video information about movies, which is one of the earliest attempts of its kind. Here, we present a deep transformer network with a probabilistic module to identify the movie genres exclusively from the poster. For experiments, we procured 13882 number of posters of 13 genres from the Internet movie database (IMDb), where our model performances were encouraging and even outperformed some major contemporary architectures.