Computing Tag-Diversity for Social Image Search

"Image search" on the basis of social tags is now a popular tool provided by image-sharing services. Some images are annotated with similar tags, while others are annotated with dissimilar ones. In this study, a concept called "tag-diversity," which represents how diverse tags are annotated to an image, is proposed, and two methods to estimate it are proposed. We conducted the experiment to investigate how the two proposed methods accurately compute tag-diversity. The results of the experiment show that both methods outperformed the baseline method, which calculates tag-diversity on the basis of the number of annotated tags. We also show some images with low and high tag-diversity, and discuss how tag-diversity can improve the current image search.