Multimodal movie genre classification using recurrent neural network

Authors

Tina Behrouzi, Ramin Toosi, Mohammad Ali Akhaee

Abstract

Genre is one of the features of a movie that defines its structure and type of audience. The number of streaming companies interested in automatically deriving movies’ genres is rapidly increasing. Genre categorization of trailers is a challenging problem because of the conceptual nature of the genre, which is not presented physically within a frame and can only be perceived by the whole trailer. Moreover, several genres may appear in the movie at the same time. The multi-label learning algorithms have not been improved as significantly as the single-label classification models, which causes the genre categorization problem to be highly complicated. In this paper, we propose a novel multi-modal deep recurrent model for movie genre classification. A new structure based on Gated Recurrent Unit (GRU) is designed to derive spatial-temporal features of movie frames. The video features are then concatenated with the audio features to predict the final genres of the movie. The proposed design outperforms the state-of-art models based on accuracy and computational cost and substantially improves the movie genre classifier system’s performance.