Image re-ranking, is an emphatic way to advance the results of web-based image search and has been accept by current economic search engines such as Bing and Google. When a query keyword is given, a list of images are first redeem based on textual dossier 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 contempt is that sometimes semantic meanings may clarify user’s search intention. Many people recently expected to match images in a semantic space which used attributes or mention classes closely associated to the semantic meanings of images as basis. In this paper, we introduce a novel image re -ranking framework, in which axiomatically offline learns different linguistic spaces for different query keywords and displays with the image particulars in the form of augmented images. The images are envisaged into their associated 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 analyze their semantic signatures access from the semantic space described by the query keyword given by the user. The expected query-specific semantic signatures significantly advance both the efficiency and capability of image re-ranking. Experimental results show that 25-40 percent related improvement has been accomplished on re-ranking precisions correlated with the state-of-the-art methods.

Image search, Image re-ranking, Semantic space, Semantic signature, Keyword extension, One click feedback

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