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Exploring Abstract Concepts for Images Privacy Classification in Social Media

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thesis
posted on 01.12.2019, 00:00 by Gabriele Galfre
Automatically detecting the private nature of images posted in social networks such as Facebook, Flickr, and Instagram, is a long-standing goal considering the pervasiveness of these networks. Several prior works to image privacy prediction showed that object tags, either manually annotated or automatically extracted from images, are highly informative about images' privacy. However, we conjecture that other aspects of images captured by abstract concepts (e.g., religion, sikhism, spirituality) can improve the performance of models that use only the concrete objects from an image (e.g., temple and person). Several experimental setups have been defined to investigate how the usage of these type of information influence the performance of privacy classifiers. Results on a Flickr dataset show that the abstract concepts are better capturing the privacy nature of images, but with concrete object tags they complement each other, yielding the best performance when used in combination as features for image privacy prediction.

History

Advisor

Caragea, Cornelia

Chair

Caragea, Cornelia

Department

Computer Science

Degree Grantor

University of Illinois at Chicago

Degree Level

Masters

Degree name

MS, Master of Science

Committee Member

Koyuncu, Erdem Magli, Enrico

Submitted date

December 2019

Thesis type

application/pdf

Language

en

Issue date

05/11/2019

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