Social Image Search with Diverse Relevance Ranking

Recent years have witnessed the success of many online social media websites, which allow users to create and share media information as well as describe the media content with tags. However, the existing ranking approaches for tag-based image search frequently return results that are irrelevant or lack of diversity. This paper proposes a diverse relevance ranking scheme which is able to simultaneously take relevance and diversity into account. It takes advantage of both the content of images and their associated tags. First, it estimates the relevance scores of images with respect to the query term based on both the visual information of images and the semantic information of associated tags. Then we mine the semantic similarities of social images based on their tags. With the relevance scores and the similarities, the ranking list is generated by a greedy ordering algorithm which optimizes Average Diverse Precision (ADP), a novel measure that is extended from the conventional Average Precision (AP). Comprehensive experiments and user studies demonstrate the effectiveness of the approach.