Abstract
Digital multimedia data is dramatically being increased everyday since the Internet became popular. This increment in multimedia data increases adult image contents to the Internet as well. Consequently, a large number of children are exposed to these X-rated contents. In this paper, we propose an efficient classification system that can categorize input images into adult or non-adult images. The simulation shows that this system achieved 95% of the true rate whereas it reduces the false positive rate below 3%.
This paper was supported by Electronics and Telecommunications Research Institute (Project No. 0801-2004-0025).
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Kim, W., Lee, HK., Yoo, S.J., Baik, S.W. (2005). Neural Network Based Adult Image Classification. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Biological Inspirations – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3696. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550822_75
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DOI: https://doi.org/10.1007/11550822_75
Publisher Name: Springer, Berlin, Heidelberg
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