DCT-based features for categorisation of social media in compressed domain
Sebastian Schmiedeke, Pascal Kelm, Thomas Sikora · 2013
These days the sharing of videos is very popular in social networks. Many of these social media websites such as Flickr, Facebook and YouTube allows the user to manually label their uploaded videos with textual information. However, the manually labelling for a large set of social media is still boring and error-prone. For this reason we present a algorithm for categorisation of videos in social media platforms without decoding them. The paper shows a data-driven approach which makes use of global and local features from the compressed domain and achieves a mean average precision of 0.2498 on the Blip10k dataset. In comparison with existing retrieval approaches at the MediaEval Tagging Task 2012 we will show the effectiveness and high accuracy relative to the state-of-the art solutions.