Tagging Driven by Interactive Image Discovery: Tagging-Tracking-Learning

With the exponential growth of web image data, image tagging is becoming crucial in many applications such as e-commerce. However, despite the great progress achieved in various image tagging technologies, none of them are able to incorporate browsing and discovery activities on web viewers in such a way that a user can easily query an image and ask the question "what is that in the image?". We have developed a comprehensive online image tagging system based on a Tagging-Tracking-Learning (TTL) framework to solve this problem. Tagging images using this system is able to turn common static web images into non-intrusive interactive images. The system tracks all browsing and interaction activities of users over time to filter out low quality tags and in turn helps the tagging process by alleviating manual operations. In this paper, we describe the implementation of the TTL framework and the novel algorithms developed. Usability studies of the system indicate that the TTL framework provides a better user experiences and simplifies the process of obtaining large tagged image collections over state-of-the-art approaches.