Due to the explosive growth of online video data and images , visual search is becoming an important area of research. Most existing approaches used text based image retrieval which is not so efficient. To precisely specify the visual documents, Visual search reranking is used. Visual search reranking is the rearrangement of visual documents based on initial search results or some external knowledge inorder to make the search efficient. Here we are making a survey of three different reranking methods 1) Reranking via Random walk over document level context graph 2) Reranking via Minimum Incremental Information Loss and 3) Reranking via Pairwise Learning and make a comparative study of it.