document: Learning to re-rank : query-dependent image re-ranking using click data Our objective is to improve the performance of keyword based image search engines by re-ranking their original results . To this end , we address three limitations of existing search engines in this paper . First , there is no straight-forward , fully automated way of going from textual queries to visual features . Image search engines therefore primarily rely on static and textual features for ranking . Visual features are mainly used for secondary tasks such as finding similar images . Second , image rankers are trained on query-image pairs labeled with relevance judgments determined by human experts . Such labels are well known to be noisy due to various factors including ambiguous queries , unknown user intent and subjectivity in human judgments . This leads to learning a sub-optimal ranker . Finally , a static ranker is typically built to handle disparate user queries . The ranker is therefore unable to adapt its parameters to suit the query at hand which again leads to sub-optimal results . We demonstrate that all of these problems can be mitigated by employing a re-ranking algorithm that leverages aggregate user click data . We hypothesize that images clicked in response to a query are mostly relevant to the query . We therefore re-rank the original search results so as to promote images that are likely to be clicked to the top of the ranked list . Our re-ranking algorithm employs Gaussian Process regression to predict the normalized click count for each image , and combines it with the original ranking score . Our approach is shown to significantly boost the performance of the Bing image search engine on a wide range of tail queries .
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