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Obscene Image Recognition Based on Model Matching and BWFNN

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Advances in Neural Networks – ISNN 2005 (ISNN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3497))

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Abstract

In this paper the obscene images first primarily recognizes through the human skin color detection and key point model matching. The other images that are not confirmed extract characteristic of obscene images through edge detection, posture estimation and wavelet compression, and then recognized using the optimizing broaden weighted fuzzy neural network, which is called two-phase recognizing method. The experiment indicates the method that this paper present can recognize the obscene images effectively.

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Author information

Authors and Affiliations

  1. College of Computer Science and Technology, Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China

    Xiaohua Liu, Zhezhou Yu, Libiao Zhang, Miao Liu, Chunguang Zhou, Chunxia Li, Catitang Sun & Li Zhang

Authors
  1. Xiaohua Liu
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  2. Zhezhou Yu
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  3. Libiao Zhang
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  4. Miao Liu
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  5. Chunguang Zhou
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  6. Chunxia Li
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  7. Catitang Sun
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  8. Li Zhang
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Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. The Key Laboratory of Optoelectric Technology & Systems, Ministry of Education, China

    Xiao-Feng Liao

  3. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

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© 2005 Springer-Verlag Berlin Heidelberg

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Cite this paper

Liu, X. et al. (2005). Obscene Image Recognition Based on Model Matching and BWFNN. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3497. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427445_48

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  • DOI: https://doi.org/10.1007/11427445_48

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25913-8

  • Online ISBN: 978-3-540-32067-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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