Pornographic image recognition and filtering using incremental learning in compressed domain

Jing Zhang; Chao Wang; Li Zhuo; Wenhao Geng

doi:10.1117/1.JEI.24.6.063002

3 November 2015 Pornographic image recognition and filtering using incremental learning in compressed domain

Jing Zhang, Chao Wang, Li Zhuo, Wenhao Geng

Author Affiliations +

Journal of Electronic Imaging, Vol. 24, Issue 6, 063002 (November 2015). https://doi.org/10.1117/1.JEI.24.6.063002

Abstract

With the rapid development and popularity of the network, the openness, anonymity, and interactivity of networks have led to the spread and proliferation of pornographic images on the Internet, which have done great harm to adolescents’ physical and mental health. With the establishment of image compression standards, pornographic images are mainly stored with compressed formats. Therefore, how to efficiently filter pornographic images is one of the challenging issues for information security. A pornographic image recognition and filtering method in the compressed domain is proposed by using incremental learning, which includes the following steps: (1) low-resolution (LR) images are first reconstructed from the compressed stream of pornographic images, (2) visual words are created from the LR image to represent the pornographic image, and (3) incremental learning is adopted to continuously adjust the classification rules to recognize the new pornographic image samples after the covering algorithm is utilized to train and recognize the visual words in order to build the initial classification model of pornographic images. The experimental results show that the proposed pornographic image recognition method using incremental learning has a higher recognition rate as well as costing less recognition time in the compressed domain.

Citation Download Citation

Jing Zhang, Chao Wang, Li Zhuo, and Wenhao Geng "Pornographic image recognition and filtering using incremental learning in compressed domain," Journal of Electronic Imaging 24(6), 063002 (3 November 2015). https://doi.org/10.1117/1.JEI.24.6.063002

Published: 3 November 2015

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