Bag of Genres for Video Retrieval
This addresses the problem of efficient video retrieval for applications like media tagging and event detection, though it is incremental as it builds on existing genre classification techniques.
The paper tackles the challenge of encoding multiple concepts or genres in videos by proposing a Bag of Genres representation, which achieves results comparable or superior to state-of-the-art methods on video genre and event retrieval tasks, with a much more compact representation.
Often, videos are composed of multiple concepts or even genres. For instance, news videos may contain sports, action, nature, etc. Therefore, encoding the distribution of such concepts/genres in a compact and effective representation is a challenging task. In this sense, we propose the Bag of Genres representation, which is based on a visual dictionary defined by a genre classifier. Each visual word corresponds to a region in the classification space. The Bag of Genres video vector contains a summary of the activations of each genre in the video content. We evaluate the proposed method for video genre retrieval using the dataset of MediaEval Tagging Task of 2012 and for video event retrieval using the EVVE dataset. Results show that the proposed method achieves results comparable or superior to state-of-the-art methods, with the advantage of providing a much more compact representation than existing features.