Multi-Label Movie Genre Detection from Movie Posters Using Deep Learning Algorithms

In the field of cinema, the concept of genre has emerged as a concept that basically includes films that have common characteristics in terms of subject matter, have adopted a common method, and have a low risk of error because they have been tried before. Identifying the genres of movies is a challenging task because genres are intangible features that are not physically present in any movie scene, so off-the-shelf image detection models may not be easily integrated into this process. In this study, we aim to address the detection of movies according to their genres using deep learning algorithms. Movie poster data of IMDB and MM-IMDB datasets were utilized in our multi-label movie genre detection studies. In our experiments, we utilized four modern pre-trained models follow as DenseNet, VGG-16, ResNet-50, and MobileNet, and evaluated their performance using performance metric values such as accuracy, precision, recall, and F-score. According to the obtained empirical results, the DenseN et architecture achieved the highest accuracy values compared to other deep learning methods in detecting multi-label movie genre detection with an impressive rates of 91.64% and 92.56%.