Web Image Retrieval Re-Ranking with Relevance Model
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This article presents a method for re-ranking images retrieved by classical search engine using key words for entering queries. This method uses the visual content of the images and it is based on the idea that the relevant images should be similar to each other while the non-relevant images should be different from each other and from relevant images. This idea has been implemented by ranking the images according to their average distances to their nearest neighbors. This query-dependent re-ranking is completed by a query-independent re-ranking taking into account the fact that some types of images are non-relevant for almost all queries. This idea is implemented by training a classifier on results from all queries in the training set. The re-ranking is successfully evaluated on classical datasets built with ExaleadTM and Google ImagesTM search engines.