Utilizing image social clues for automated image tagging

Social tags have been successfully utilized for image search and recommendation, yet the tags may be bias and noisy. Assisting users to annotate their images with tags that meet their preferences and efficiently describe the visual content is a fundamental objective in multimedia. In this work, we propose to leverage the image social information, such as tagging preferences of an image owner and social groups that an image has been shared with, by adopting the well-known neighbor voting approach for automated image tagging. In specific, we assign more contributions of neighborhood images which are socially closer to the target image in the voting procedure. Meanwhile, the social strength of reference images with respect to the target image is jointly considered. The experiments on a large scale image dataset for tag recommendation and image search show the advantages of considering image social clues.