An Image Tag Recommendation Approach Combining Relevance with Diversity
Cui Chao · Chinese Journal of Computers · 2013
To help users organize and retrieve the image resources efficiently,most image sharing sites allow users to annotate the images with tags.Image tag recommendation systems aim to provide a set of tag candidates to facilitate the tagging process done by users.Previous image tag recommendation methods are usually developed based on tag co-occurrence information.However,due to the neglect of the visual information associated with images and the semantic diversity among recommended tags,the recommendation results of previous methods often suffer from the problems of tag ambiguity and redundancy.To solve the above problems,this paper proposes a novel image tag recommendation approach,which considers both the relevance and diversity of the recommended tags.First,the approach employs the visual language model to calculate the relevance between a tag and an image,as well as the visual distance between two tags.Then,according to the above calculations,a greedy search algorithm is proposed to find a tag set as the final recommendation,which reaches a reasonable trade-off between the relevance and diversity.Experiments on Flickr data set show the proposed approach outperforms the state-of-the-art methods in terms of precision,topic coverage and F1 value.