Learning Image Re-Rank: Query-Dependent Image Re-Ranking Using Semantic Signature

Image re-ranking, is an effective way to improve the results of web-based image search and has been adopted by current commercial search engines such as Bing and Google. When a query keyword is given, a list of images are first retrieved based on textual information given by the user. By asking the user to select a query image from the pool of images, the remaining images are re-ranked based on their index with the query image. A major challenge is that sometimes semantic meanings may interpret user’s search intention. Many people recently proposed to match images in a semantic space which used attributes or reference classes closely related to the semantic meanings of images as basis. In this paper, we propose a novel image re-ranking framework, in which automatically offline learns different semantic spaces for different query keywords and displays with the image details in the form of augmented images. The images are projected into their related semantic spaces to get semantic signatures with the help of one click feedback from the user. At the online stage, images are re-ranked by comparing their semantic signatures obtained from the semantic space specified by the query keyword given by the user. The proposed query-specific semantic signatures significantly improve both the accuracy and efficiency of image re-ranking. Experimental results show that 25-40 percent relative improvement has been achieved on re-ranking precisions compared with the state-of-the-art methods.