DocumentCode :
261413
Title :
An improved logo detection method with learning-based verification for video classification
Author :
Hyo-Young Kim ; Mun-Cheon Kang ; Sung-Ho Chae ; Dae-Hwan Kim ; Sung-Jea Ko
Author_Institution :
Dept. of Electron. Eng., Korea Univ., Seoul, South Korea
fYear :
2014
fDate :
7-10 Sept. 2014
Firstpage :
192
Lastpage :
193
Abstract :
With the growth of cloud services, concerns have been raised regarding illegal sharing of the commercial video. To prevent the illegal sharing automatically, the method for classifying video as `commercial´ or `noncommercial´ is essentially required. Since most commercial video has a logo as a visible watermark, automatic logo detection can be an efficient method for the video classification. In this paper, we present an improved logo detection method which correctly detects the logo in any types of video using learning-based logo verification. Experimental results show that the proposed method achieves improved detection performance as compared with the existing method, and thus can be effectively used for classifying the video.
Keywords :
feature extraction; image classification; video watermarking; automatic logo detection; cloud services; commercial video; learning-based verification; logo detection method; video classification; visible watermark; SVM; Video classification; copyright protection; feature extraction; logo detection;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Consumer Electronics ??? Berlin (ICCE-Berlin), 2014 IEEE Fourth International Conference on
Conference_Location :
Berlin
Type :
conf
DOI :
10.1109/ICCE-Berlin.2014.7034299
Filename :
7034299
Link To Document :
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