Social Image Tag Relevance Learning based on Pixel Voting

Abstract

Social image platforms allow their users sharing and searching their photos based on images' tags. These tags are provided by different users. Inevitably, the tags are spontaneously ambiguous, and personalized. So, learning the relevance between tags and images is playing an important role in tag-based retrieval systems. Choosing visual neighbors for seed images as voters is a widely used method for learning tag relevance. However, most existing methods of choosing visual neighbors for seed images are based on the global features of the whole images, ignoring the local features. In this paper we propose a pixel voting method to choose the visual neighbors for seed images. Experiment shows that this method is a more natural way to measure the similarity of images. Based the selected neighbors we learn the tag relevance, and the experiment on the MIR Flickr dataset shows that our algorithm is effective in tag de-noising and tag ranking.