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.
REF proposed a Group-Based Personalized Model for image privacy classification in online social media sites that learns a set of archetypical privacy models (groups) and associates a given user with one of these groups.
A Group-Based Personalized Model for Image Privacy Classification and Labeling.
IJCAI
Computer Science