Improving Web Image Search using Meta Re-rankers

The previous methods for image search reranking suffer from the unreliability of the assumptions under which the initial text based image search result is employed in the reranking process. In our proposed system, prototype-based reranking method is suggested address this problem in scalable fashion. This typical assumption that the top-images in the text-based search result are equally relevant is relaxed by linking the relevance of the images to their initial rank positions. The number of images is employed by the initial search result as the prototypes that serve to visually represent the query and that are subsequently used to construct Meta re-rankers. For applying different Meta re-rankers to an image from the initial result, then the re-ranking scores are generated, which are then aggregated by using a linear model to produce the final relevance score and the new rank position for an image in the re-ranked search result. It is improving the performance over the text-based image search engine. Key concepts: Prototype based Meta re-ranker, Text based search Image Re-ranking.