Uncovering scene context for predicting privacy of online shared images

Ashwini Tonge, Cornelia Caragea, Anna Squicciarini

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

With the exponential increase in the number of images that are shared online every day, the development of effective and efficient learning methods for image privacy prediction has become crucial. Prior works have used as features automatically derived object tags from images' content and manually annotated user tags. However, we believe that in addition to objects, the scene context obtained from images' content can improve the performance of privacy prediction. Hence, we propose to uncover scene-based tags from images' content using convolutional neural networks. Experimental results on a Flickr dataset show that the scene tags and object tags complement each other and yield the best performance when used in combination with user tags.

Original languageEnglish (US)
Title of host publication32nd AAAI Conference on Artificial Intelligence, AAAI 2018
PublisherAAAI press
Pages8167-8168
Number of pages2
ISBN (Electronic)9781577358008
StatePublished - Jan 1 2018
Event32nd AAAI Conference on Artificial Intelligence, AAAI 2018 - New Orleans, United States
Duration: Feb 2 2018Feb 7 2018

Publication series

Name32nd AAAI Conference on Artificial Intelligence, AAAI 2018

Other

Other32nd AAAI Conference on Artificial Intelligence, AAAI 2018
CountryUnited States
CityNew Orleans
Period2/2/182/7/18

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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  • Cite this

    Tonge, A., Caragea, C., & Squicciarini, A. (2018). Uncovering scene context for predicting privacy of online shared images. In 32nd AAAI Conference on Artificial Intelligence, AAAI 2018 (pp. 8167-8168). (32nd AAAI Conference on Artificial Intelligence, AAAI 2018). AAAI press.