Movie genre classification with Convolutional Neural Networks

Abstract

Automatic pattern recognition from videos is a high-complexity task, and well-established Machine Learning algorithms have difficulties in handling it in an efficient and effective fashion. Convolutional Neural Networks are the state-of-the-art method for supervised image classification, borrowing concepts from image processing in order to ensure some degree of scale and position invariance. They are capable of detecting primary features, which are then combined by subsequent layers of the CNN architecture, resulting in the detection of higher-order complex and relevant novel features. Considering that a video is a set of ordered images in time, we propose in this paper to explore CNNs in the context of movie trailers genre classification. Our contributions are twofold. First, we have developed a novel movie trailers dataset with more than 3500 trailers whose genres are known, and we make it publicly available for the interested reader. Second, we detail a novel classification method that encapsulates a CNN architecture to perform movie trailer genre classification, namely CNN-MoTion, and we compare it with state-of-the-art feature extraction techniques for movie classification such as Gist, CENTRIST, w-CENTRIST, and low-level feature extraction. Results show that our novel method significantly outperforms the current state-of-the-art approaches.