Research Of Image Annotation And Tag Recommendation On Shared Resource Websites

Nowadays, the number of internet images is growing at an exponential rate. Therefore, how to effectively manage and retrieve large scale Internet images put forth a great challenge. Since a great number of images uploaded onto Internet do not have any labels, or has limited labels with noise, automatic annotation of such "weakly-tagged" Internet images has been a hot topic recently.Since users intend to recommend images to multiple social groups according to semantics of images when they upload images into Flicker, this paper proposes a two-stage approach to automatically annotate weakly-tagged social images. The first stage discovers the latent topics in each group by Latent Dirichlet Allocation(LDA) model, and filters out noisy tags in group level in order to re-rank topic-relevant tags. The second stage discovers the hierarchical topic structure among multiple groups by WordNet, and hierarchically fuses the candidate tags from multiple groups.This paper also proposes an approach to integrate social text, image and user context for tag recommendation. This approach sets up a ternary matrix to represent the relationship among users, images and tags at first; and get a personal preference by discovering users with similar interest and images with similar visual similarity at second, and finally utilizes random walk to recommend tags for unlabeled images. This tag recommend approach is very flexible, since we can get recommendation result once any one of information about a user, an image, or a tag is offered.