Context-aware mobile image annotation for media search and sharing

In recent years, rapid advances in media technology including acquisition, processing and distribution have led to proliferation of many mobile applications. Amongst them, one of the emerging applications is mobile-based image annotation that uses camera phones to capture images with system-suggested tags before uploading them to the media sharing portals. This procedure can offer information to mobile users and also facilitate the retrieval and sharing of the image for Web users. However, context information that can be acquired from mobile devices is underutilized in many existing mobile image annotation systems. In this paper, we propose a new mobile image annotation system that utilizes content analysis, context analysis and their integration to annotate images acquired from mobile devices. Specifically, three types of context, location, user interaction and Web, are considered in the tagging processes. An image dataset of Nanyang Technological University (NTU) campus has been constructed, and a prototype mobile image tag suggestion system has been developed. The experimental results show that the proposed system performs well in both effectiveness and efficiency on NTU dataset, and shows good potential in domain-specific mobile image annotation for image sharing.