Personalized automatic image annotation based on reinforcement learning

With the rapidly increasing number of personal image collections on the web, it is of great importance to annotate these user-uploaded images in personalized manner. But personalized image annotation is largely ignored by the mainstream of image annotation research. In this paper, we focus on personalizing the automatic image annotation by proposing a general framework which jointly exploits the generic content-based image annotation, personal image tagging history and the content of personal history images. In our framework, two sets of candidate annotations are extracted for each image based on content-based annotation and personal image tagging history. Considering that the user´s interest may not stay the same, when exploiting the personal image tagging history, we also take the content of personal history images into account to avoid the noise. To get the final annotations, we propose an unsupervised algorithm based on reinforcement learning to combine the above two candidate annotation sets. Encouraging results show that the proposed framework is effective and promising for personalizing automatic image annotation.