conference paper

Conference/Proceedings15th IEEE International Workshop on Multimedia Signal Processing
Start date30.09.2013
End date02.10.2013
Author(s)Sebastian Schmiedeke, Pascal Kelm, Thomas Sikora
TitleDCT-based Features for Categorisation of Social Media in Compressed Domain
AbstractThese 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 fast 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.
Key wordsmultimedia analysis, Genre classification, bag of words, compressed domain features
NoteISBN 978-1-4799-0125-8

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