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- W2392945574 abstract "An important problem in image search is how to efficiently the search results.However,the ranking models of existing image search engines are generally based on associated text while neglecting the visual content.Because sometimes the textual features cannot match the actual visual content,the search results will be mixed with some false images.In order to fix this problem,visual re-ranking has been proposed recently,in which visual information is applied to refine text based search results.However,the improvement for effectiveness brought by visual re-ranking is limited.This is mainly caused by that the errors in the text-based results will propagate to the refinement stage in visual re-ranking.In this work,we propose a Joint Visual and Text information for Learning to Ranking(JLR) model based on to rank framework,in which textual and visual information are simultaneously leveraged in one step for ranking learning processing.This only processing step can avoid the error propagation.We formulate the JLR based on large margin structured output learning,by modeling the visual information into a regularization term.Because the number of constraints is huge,direct optimization of the learning problem is nearly infeasible.The efficient cutting plane algorithm is adopted to learn the model by iteratively adding the most violated constraints.The experimental results demonstrate that the proposed ranking model significantly outperforms the state-of-the-art re-ranking methods." @default.
- W2392945574 created "2016-06-24" @default.
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- W2392945574 date "2011-01-01" @default.
- W2392945574 modified "2023-09-24" @default.
- W2392945574 title "Joint Text and Visual Information with Learning to Rank for Image Search" @default.
- W2392945574 hasPublicationYear "2011" @default.
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