Context-based image re-ranking for content-based image retrieval

In the area of content based image retrieval, people always use the image similarity based on the concrete image parameters like color to rank the images. However the ranking criteria based on image similarity directly is not so significant enough because many images in the given large-scale image database have the approximate similarities to a given image. We propose a graph-based mutual reinforcement method which utilize both of the inter- and intra- relationships among the content and context of the images for re-ranking the similar images. After the re-ranking, we could enlarge the relative-ranking-score-difference of the images, so that the search result becomes more significance. On the other hand our method could also improve the quality of the search result on the metrics such as MAP, recall and precision. The experiments based on the images from the social images hosting websites show the efficiency of our method.