Demystifying Visual Features of Movie Posters for Multi-Label Genre Identification

TL;DR

This paper introduces a deep transformer-based method for automatically identifying multiple movie genres solely from poster images, providing a pre-release tool for marketing and recommendation systems.

Contribution

It presents one of the first models to classify multiple genres from posters without using textual or metadata information, outperforming some existing architectures.

Findings

Model achieved high accuracy on IMDb posters

Outperformed several contemporary architectures

Demonstrated effectiveness of transformer networks for image-based genre classification

Abstract

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 OTT (over-the-top) 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 pre-release implicit information to generate mass interest. In this paper, we work for automated multi-label 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.