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互联网社群图像标签排序研究进展

Advances in Tag Ranking for Internet Social Images
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摘要 互联网社群图像标签排序是目前计算机视觉、机器学习等领域最热门的课题之一。图像标签序列的合理性直接影响到图像检索等应用的效果。目前图像标签排序的方法多种多样,根据标签排序方法的不同将其划分为基于语义相关度与基于视觉显著性的标签排序,着重介绍了两类方法的典型标签排序方法,分析其各自的优缺点。最后就图像标签排序的评价方法以及发展趋势做了简单的论述。 Tag ranking for internet social images is one of the most popular topics in the computer vision and machine learning. The effect of image retrieval and other applications is directly affected by the reasonableness of the order of image tag. Currently, the existing methods on tag ranking are various. This paper divided them into relevance-based tag ranking and saliency-based tag ranking. This paper highlighted two typical image tag ranking ways and analyzes their advantages and disadvantages respectively. Finally, we discussed the evaluation methods and trends of image tag ranking simply.
作者 吴焰樟 刘宏哲 冯松鹤 袁家政 张静怡
机构地区 北京联合大学北京市信息服务工程重点实验室 北京交通大学计算机与信息技术学院
出处 《计算机科学》 CSCD 北大核心 2015年第8期22-27,35,共7页 Computer Science
基金 国家自然科学基金(61372148 61271369 41101111) 北京市教育委员会科技发展计划面上项目(SQKM201411417004) 北京联合大学人才强校计划人才资助项目(BPHR2014A04 BPHR2014E02) 北京市属高等学校创新团队建设与教师职业发展计划项目(CIT&TCD20130513 IDHT20140508)资助
关键词 标签排序 相关性 显著性 Tag ranking,Relevance, Saliency
分类号 TP391 [自动化与计算机技术—计算机应用技术]
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参考文献40

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二级参考文献18

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计算机科学
2015年 第8期

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