Tagging is a way to organize huge image collections. This work explores manual and automatic image tagging on mobile devices.

Tag Your Emotions: A Novel Mobile User Interface for Annotating Images with Emotions Nina Runge, Dirk Wenig, Marius Hellmeier and Rainer Malaka

People tend to collect more and more data, this is especially true for images on mobile devices. Tagging images is a good way to sort such collections. While automatic tagging systems are often focused on the content, such as objects or persons in the image, manual annotations are very important to describe the context of an image. Often especially emotions are important, e.g., when a person reflects a situation, shows images from a very personal collection to others, or when using images to illustrate presentations. Unfortunately, manual annotation is often very boring and users are not very motivated to do so. While there are many approaches to motivate people to annotate data in a conventional way, none of them has focused on emotions. In this poster abstract, we present EmoWheel; an innovative interface to annotate images with emotional tags. We conducted a user study with 18 participants. Results show that the EmoWheel can enhance the motivation to annotate images.

Nina Runge, Dirk Wenig, Marius Hellmeier and Rainer Malaka (2016) Tag Your Emotions: A Novel Mobile User Interface for Annotating Images with Emotions In MobileHCI '16: Proceedings of the 18th International Conference on Human-Computer Interaction with Mobile Devices and Services, 846-853. ACM, New York, NY, USA. 10.1145/2957265.2961836 | BibTex

Keep an Eye on Your Photos: Automatic Image Tagging on Mobile Devices Nina Runge, Dirk Wenig and Rainer Malaka

In this paper we present how to tag images automatically based on the image and sensor data from a mobile device. We developed a system that computes low-level tags using the image itself and meta data. Based on these tags and previous user tags we learn high-level tags. With a client-server-implementation we source out computational expensive algorithms to recommend the tags as fast as possible. We show what are the best feature extraction methods in combination with a machine learning technique to recommend good tags.

Nina Runge, Dirk Wenig and Rainer Malaka (2014) Keep an Eye on Your Photos: Automatic Image Tagging on Mobile Devices In MobileHCI '14: Proceedings of the 16th International Conference on Human-Computer Interaction with Mobile Devices & Services, 513-518. ACM, New York, NY, USA. 10.1145/2628363.2634225 | BibTex