Summarizing social image search results

Most existing social image search engines present search results as a ranked list of images, which cannot be consumed by users in a natural and intuitive manner. Here, we present a novel algorithm that exploits both visual features and tags of the search results to generate high quality image search result summary. The summary not only breaks the results into visually and semantically coherent clusters, but it also maximizes the coverage of the original search results. We demonstrate the effectiveness of our method against state-of-the-art image summarization and clustering algorithms.