Movie Posters’ Classification into Multiple Genres

Abstract

Our project intends to classify movies into the three most probable genres that they belong to, from a predefined set of 25 genres, based on only one image i.e the movie poster. We have made use of Convolutional Neural Networks (CNN) to realize this project as we believe it would be of help to extract the features and visual information from the image. Instead of a multi-class classification problem in which the input is classified into any one class, this project would be more correctly described as a multilabel classification problem as a movie belongs to more than one genre. In this project we see a comparative study of different architectures and tune them to yield the best result based on the metric of accuracy. We have applied various techniques such as data augmentation and L2 regularization to comparatively deduce the model that performs best from all the tested models.