Image auto-annotation by exploiting web information

We consider the image auto-annotation problem by exploiting information from Internet. Given a collection of semantically similar images and a keyword that accurately describes these images, our goal is to find a set of popular tags to annotate each image, conforming to those used for similar images found on the web. We propose a novel framework to exploit classification based learning and bipartitioning clustering algorithms for extracting meaningful tags from semantical images on the web. Specifically, we adopt multiple kernel learning (MKL) to first select relevant images with their associated tags, which are obtained from the web based on keyword search, and then build a bipartite graph to model the relation between related tags and images. Finally, we partition over the bipartite graph to produce a set of significant tags for each image. We evaluate our proposed method by using the colorful Natural Scene and Events datasets to generate related images and tags from the Flickr website. The experimental results show that our proposed method has superior performance compared with baseline methods.