Pornographic image recognition by strongly-supervised deep multiple instance learning

Yuhui Wang, Xin Jin, Xiaoyang Tan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

23 Scopus citations

Abstract

In this paper, we propose a principled framework for pornographic image recognition. Specifically, we present our definition of pornographic images, which characterizes the pornographic contents in images as the exposure of private body parts. As the private body parts often lie in local image regions, we model each image as a bag of local image patches (instances), and assume that for each pornographic image at least one instance accounts for the pornographic content within it. This treatment allows us to cast the model training as a Multiple Instance Learning (MIL) problem. Furthermore, we propose a strongly-supervised setting for MIL by identifying the most likely pornographic instances in positive bags, which effectively prevents the algorithm from getting trapped in a bad local optima. Last but not least, we formulate our strongly-supervised MIL under the deep CNN framework to learn deep representations; hence we call it Strongly-supervised Deep MIL (SD-MIL). We demonstrate that our SD-MIL based system produces remarkable accuracy with 97.01% TPR at 1% FPR, testing on 117K pornographic images and 117K normal images from our newly-collected large scale dataset.

Original languageEnglish (US)
Title of host publication2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings
PublisherIEEE Computer Society
Pages4418-4422
Number of pages5
ISBN (Electronic)9781467399616
DOIs
StatePublished - Aug 3 2016
Event23rd IEEE International Conference on Image Processing, ICIP 2016 - Phoenix, United States
Duration: Sep 25 2016Sep 28 2016

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2016-August
ISSN (Print)1522-4880

Conference

Conference23rd IEEE International Conference on Image Processing, ICIP 2016
Country/TerritoryUnited States
CityPhoenix
Period09/25/1609/28/16

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

Keywords

  • Deep learning
  • Multiple Instance Learning
  • Pornographic image recognition

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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