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DCT-based Features for Categorisation of Social Media in Compressed Domain
Citation key 1426Schmiedeke2013
Author Sebastian Schmiedeke and Pascal Kelm and Thomas Sikora
Title of Book 15th IEEE International Workshop on Multimedia Signal Processing
Pages 295–300
Year 2013
DOI 10.1109/MMSP.2013.6659304
Month sep
Note ISBN 978-1-4799-0125-8 Copyright and Reprint Permission: Abstracting is permitted with credit to the source. libraries are permitted to photocopy beyond the limit of U.S. copyright law for private use of patrons those articles in this volumen that carry a code at the bottom of the first page, provided the per-copy fee indicated in the code is paid through Copyright Clearance Center, 222 Rosewood Drive, Danvers, MA 01923. For other copying, reprint or republication permission, write to IEEE Copyrights Manager, IEEE Operations Center, 445 Hoes lane, Piscataway, NJ 08854. All rights reserved. Copyright ©2013 IEEE.
Editor IEEE
Publisher IEEE
Organization IEEE
Abstract 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 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.
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