Description : Image search engines play the role of a bridge between user intentions and visual images. By simply representing user intentions with textual query, many existing research works have been focusing on how to match the textual query with visual images and their surrounding texts or tags. However, the returned results are often unsatisfactory due to their deviation from user intentions. , particularly for queries with heterogeneous concepts (such as “apple”, “jaguar”) or general (non-specific) concepts (such as “landscape”, “vacation”). In this project, we exploit social data from social media platforms to assist image search engines, aiming to improve the relevance between returned images and user intentions (i.e., social relevance).Socio-Visual Algorithm is implemented on top of Flickr Rank to re-rank web images based on community knowledge. By considering social relevance and visual relevance comprehensively, we can understand user intention better, thereby improving the performance of our image ranking approach.