Re-ranking of images is an effective way to improve the results of web-based image search and it has been adopted by the current search engines. When a query keyword is given, a list of images is retrieved first on the basis of textual information given by the user. Nowadays, with user’s search intention, web search engine gives a set of relevant as well as irrelevant Images and then the user selects query image from the pool. The remaining Images are re-ranked based on their visual similarities. The main drawback with current web search engine is that visual similarities of images do not correlate well with Images semantic meaning. We propose a unique web Image re-ranking framework that provides learned Images visual and semantic meaning based on query keywords.
Semantic meaning of Images, content based-image retrieval, keyword expansion, image re-ranking.
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