A visual-based late-fusion framework for video genre classification

In this paper we investigate the performance of visual features in the context of video genre classification. We propose a late-fusion framework that employs color, texture, structural and salient region information. Experimental validation was carried out in the context of the MediaEval 2012 Genre Tagging Task using a large data set of more than 2,000 hours of footage and 26 video genres. Results show that the proposed approach significantly improves genre classification performance outperforming other existing approaches. Furthermore, we prove that our approach can help improving the performance of the more efficient text-based approaches.