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Conference Paper Obscene Video Detection by Multiple-Classifier Fusion
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Authors
Jin-Woo Choi, Seungwan Han
Issue Date
2015-01
Citation
International Conference on Advanced Communication Technology (ICACT) 2015, pp.1-4
Language
English
Type
Conference Paper
DOI
https://dx.doi.org/10.1109/FCV.2015.7103753
Project Code
14MS2700, Development of the Filtering Technology for Objectionable Streaming Contents on Smart Platform, Han Seung Wan
Abstract
Obscene video detection is a core technology to prevent inappropriate access of children or teenagers to obscene video contents. There are many obscene video or image detection methods such as skin region analysis based methods, global histogram based methods. However, accuracy of these methods are not high enough to be deployed in the real-world environment. In this paper, we propose an obscene video detection method by multiple-classifier fusion. Three fusion methods are proposed: precision-oriented, recall-oriented, and accuracy-oriented. Experimental results show that by using the multiple classifier fusion method, superior accuracy, precision and recall can be achieved while exploiting the complementary behavior of different obscene classifiers.
KSP Keywords
Detection Method, Global histogram, Multiple classifier fusion, Precision and recall, Real-world, Skin Region Analysis, Video contents, Video detection, fusion method, image detection, recall-oriented