By Topic

Pornographic images detection using High-Level Semantic features

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

6 Author(s)
Lintao Lv ; Sch. of Comput. Sci. & Eng., Xi'an Univ. of Technol., Xi'an, China ; Chengxuan Zhao ; Hui Lv ; Jin Shang
more authors

The pornographic images recognition can be seen as a special kind of object recognition task,but current pornographic images filtering algorithms using BoVF approaches have some problems,such as the high false positive rate toward the bikinis images and insufficiency of filtering pornographic images with pornographic actions. The paper proposes a novel pornographic image filtering model using High-level Semantic features. Firstly, we optimize BoVW model to minimize semantic gap between low-level features and high-level semantic features and then high-level semantic dictionary is constructed by fusing the context of the visual vocabularies and spatial-related high-level semantic features of pornographic images. Experimental results show that the model has an accuracy up to 87.6% when testing the multi-person pornographic images, which is significantly higher than the existing pornographic images filtering algorithm based on Bag-Of-Visual-Words.

Published in:

Natural Computation (ICNC), 2011 Seventh International Conference on  (Volume:2 )

Date of Conference:

26-28 July 2011