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Combining re-ranking and rank aggregation methods for image retrieval

机译:结合重排序和排序聚合方法进行图像检索

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摘要

This paper presents novel approaches for combining re-ranking and rank aggregation methods aiming at improving the effectiveness of Content-Based Image Retrieval (CBIR) systems. Given a query image as input, CBIR systems retrieve the most similar images in a collection by taking into account image visual properties. In this scenario, accurately ranking collection images is of great relevance. Aiming at improving the effectiveness of CBIR systems, re-ranking and rank aggregation algorithms have been proposed. However, different re-ranking and rank aggregation approaches, applied to different image descriptors, may produce different and complementary image rankings. In this paper, we present four novel approaches for combining these rankings aiming at obtaining more effective results. Several experiments were conducted involving shape, color, and texture descriptors. The proposed approaches are also evaluated on multimodal retrieval tasks, considering visual and textual descriptors. Experimental results demonstrate that our approaches can improve significantly the effectiveness of image retrieval systems.
机译:本文提出了结合重排序和排序聚合方法的新方法,旨在提高基于内容的图像检索(CBIR)系统的有效性。给定查询图像作为输入,CBIR系统通过考虑图像的视觉属性来检索集合中最相似的图像。在这种情况下,对收藏图像进行准确排名非常重要。为了提高CBIR系统的有效性,已经提出了重新排序和排序聚合算法。但是,应用于不同图像描述符的不同重新排名和排名聚合方法可能会产生不同且互补的图像排名。在本文中,我们提出了四种新颖的方法来组合这些排名,以期获得更有效的结果。进行了一些涉及形状,颜色和纹理描述符的实验。还考虑了视觉和文本描述符,对多模式检索任务进行了评估。实验结果表明,我们的方法可以显着提高图像检索系统的有效性。

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