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Learning to re-rank: query-dependent image re-ranking using click data

Learning to re-rank: query-dependent image re-ranking using click data,10.1145/1963405.1963447,Vidit Jain,Manik Varma

Learning to re-rank: query-dependent image re-ranking using click data   (Citations: 2)
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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.
Conference: World Wide Web Conference Series - WWW , pp. 277-286, 2011
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    • ...To better exploit the underlying relationship between video shots, their proposed re-ranking scheme simultaneously leverages textual relevancy, semantic concept relevancy, and low-levelfeature-based visual similarity.According to new research, Vidit Jain and Manik Varma.[4] their objective is using the number of images clicked in response to a query to improve the performance of keyword based image search engines by re-ranking their ...

    Ruofan Wanget al. Re-ranking search results using semantic similarity

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