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Journal of Central South University

, Volume 23, Issue 6, pp 1383–1389 | Cite as

Bag-of-visual-words model for artificial pornographic images recognition

  • Fang-fang Li (李芳芳)
  • Si-wei Luo (罗四伟)
  • Xi-yao Liu (刘熙尧)Email author
  • Bei-ji Zou (邹北骥)
Mechanical Engineering, Control Science and Information Engineering

Abstract

It is illegal to spread and transmit pornographic images over internet, either in real or in artificial format. The traditional methods are designed to identify real pornographic images and they are less efficient in dealing with artificial images. Therefore, criminals turn to release artificial pornographic images in some specific scenes, e.g., in social networks. To efficiently identify artificial pornographic images, a novel bag-of-visual-words based approach is proposed in the work. In the bag-of-words (BoW) framework, speeded-up robust feature (SURF) is adopted for feature extraction at first, then a visual vocabulary is constructed through K-means clustering and images are represented by an improved BoW encoding method, and finally the visual words are fed into a learning machine for training and classification. Different from the traditional BoW method, the proposed method sets a weight on each visual word according to the number of features that each cluster contains. Moreover, a non-binary encoding method and cross-matching strategy are utilized to improve the discriminative power of the visual words. Experimental results indicate that the proposed method outperforms the traditional method.

Keywords

artificial pornographic image bag-of-words (BoW) speeded-up robust feature (SURF) descriptors visual vocabulary 

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Copyright information

© Central South University Press and Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Fang-fang Li (李芳芳)
    • 1
  • Si-wei Luo (罗四伟)
    • 1
  • Xi-yao Liu (刘熙尧)
    • 1
    Email author
  • Bei-ji Zou (邹北骥)
    • 1
  1. 1.School of Information Science and EngineeringCentral South UniversityChangshaChina

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