PRISM: Concept-preserving Summarization of Top-K Social Image Search Results

Most existing tag-based 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. In this demonstration, we present a novel concept-preserving image search results summarization system called prism. prism exploits both visual features and tags of the search results to generate high quality summary, which not only breaks the results into visually and semantically coherent clusters but it also maximizes the coverage of the original top-k search results. It first constructs a visual similarity graph where the nodes are images in the top-k search results and the edges represent visual similarities between pairs of images. This graph is optimally decomposed and compressed into a set of concept-preserving subgraphs based on a set of summarization criteria. One or more exemplar images from each subgraph is selected to form the exemplar summary of the result set. We demonstrate various innovative features of prism and the promise of superior quality summary construction of social image search results.