A Group-Based Personalized Model for Image Privacy Classification and Labeling
Abstract
We address machine prediction of an individual's label (private or public) for a given image. This problem is difficult due to user subjectivity and inadequate labeled examples to train individual, personalized models. It is also time and space consuming to train a classifier for each user. We propose a Group-Based Personalized Model for image privacy classification in online social media sites, which learns a set of archetypical privacy models (groups), and associates a given user with one of these groups. Our system can be used to provide accurate ``early warnings'' with respect to a user's privacy awareness level.
Cite
Text
Zhong et al. "A Group-Based Personalized Model for Image Privacy Classification and Labeling." International Joint Conference on Artificial Intelligence, 2017. doi:10.24963/IJCAI.2017/552Markdown
[Zhong et al. "A Group-Based Personalized Model for Image Privacy Classification and Labeling." International Joint Conference on Artificial Intelligence, 2017.](https://mlanthology.org/ijcai/2017/zhong2017ijcai-group/) doi:10.24963/IJCAI.2017/552BibTeX
@inproceedings{zhong2017ijcai-group,
title = {{A Group-Based Personalized Model for Image Privacy Classification and Labeling}},
author = {Zhong, Haoti and Squicciarini, Anna Cinzia and Miller, David J. and Caragea, Cornelia},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2017},
pages = {3952-3958},
doi = {10.24963/IJCAI.2017/552},
url = {https://mlanthology.org/ijcai/2017/zhong2017ijcai-group/}
}