Tag ranking
Tag ranking
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DOI：
10.1145/1526709.1526757
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发表时间：
2009-04
期刊：
影响因子：
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通讯作者：
Dong Liu;Xian-Sheng Hua;Linjun Yang;Meng Wang;HongJiang Zhang
中科院分区：
文献类型：
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作者：
Dong Liu;Xian-Sheng Hua;Linjun Yang;Meng Wang;HongJiang Zhang
Flickr等社交媒体共享网站允许用户使用免费标签注释图像，这大大方便了Web图像搜索和组织。然而，与图像相关联的标签通常是随机顺序的，没有任何重要性或相关性信息，这限制了这些标签在搜索和其他应用中的有效性。在本文中，我们提出了一个标签排名计划，旨在自动排名的标签与给定的图像，根据其相关性的图像内容。我们首先基于概率密度估计来估计标签的初始相关性分数，然后在标签相似性图上执行随机游走以细化相关性分数。在50000张Flickr照片集上的实验结果表明，该方法是有效的。我们还将标签排名应用到三个应用中：（1）基于标签的图像搜索，（2）标签推荐，和（3）组推荐，这表明所提出的标签排名方法确实提高了社交标签相关应用的性能。
Social media sharing web sites like Flickr allow users to annotate images with free tags, which significantly facilitate Web image search and organization. However, the tags associated with an image generally are in a random order without any importance or relevance information, which limits the effectiveness of these tags in search and other applications. In this paper, we propose a tag ranking scheme, aiming to automatically rank the tags associated with a given image according to their relevance to the image content. We first estimate initial relevance scores for the tags based on probability density estimation, and then perform a random walk over a tag similarity graph to refine the relevance scores. Experimental results on a 50, 000 Flickr photo collection show that the proposed tag ranking method is both effective and efficient. We also apply tag ranking into three applications: (1) tag-based image search, (2) tag recommendation, and (3) group recommendation, which demonstrates that the proposed tag ranking approach really boosts the performances of social-tagging related applications.