The impact of abstract and object tags on image privacy classification
This work addresses the challenge of improving image privacy classifiers for users and developers, but it is incremental as it builds on existing tag-based methods.
The paper tackled the problem of image privacy classification by comparing the effectiveness of object tags versus abstract tags, finding that abstract tags are more effective with limited tag budgets while object tags perform equally well with more tags.
Object tags denote concrete entities and are central to many computer vision tasks, whereas abstract tags capture higher-level information, which is relevant for tasks that require a contextual, potentially subjective scene understanding. Object and abstract tags extracted from images also facilitate interpretability. In this paper, we explore which type of tags is more suitable for the context-dependent and inherently subjective task of image privacy. While object tags are generally used for privacy classification, we show that abstract tags are more effective when the tag budget is limited. Conversely, when a larger number of tags per image is available, object-related information is as useful. We believe that these findings will guide future research in developing more accurate image privacy classifiers, informed by the role of tag types and quantity.