Automatic image tag ranking scheme based on visual content semantic relatedness

Abstract

An automatic image tag ranking scheme was proposed based on visual content semantic relatedness, which sorted the tags of the community-contributed images according to the semantic relatedness between tags and image contents. Firstly, the semantic relatedness calculation between tag and image content was formulated as a probabilistic problem based on Bayes' theorem. Then, multiple visual features were fused to obtain reasonable probability evaluation for tag-image content relatedness in different semantic themes. This method has high scalability. Extensive experiments were conducted over a dataset consisting of 149 915 images downloaded from Flickr and experimental results demonstrate the effectiveness of the proposed method.