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使用距离度量学习挖掘社交图像以实现自动图像标记
Mining Social Images with Distance Metric Learning for Automated Image Tagging |
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| 课程网址: |
http://videolectures.net/wsdm2011_hoi_msi/
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| 主讲教师: |
Steven C. H. Hoi
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| 开课单位: |
南洋理工大学
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| 开课时间: |
2011-08-09
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| 课程语种: |
英语
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| 中文简介: |
随着各种社交媒体应用的普及，如今很多社交媒体网站上都出现了大量带有高质量标签的社交图片。网络社交图像的挖掘已经成为网络搜索和数据挖掘领域一个新兴的重要研究课题。本文提出了一种挖掘社会图像的机器学习框架，并对其在图像自动标注中的应用进行了研究。有效地发现知识从社会图像通常与多通道相关内容(包括视觉图像和文本标签),我们提出一个新的统一的距离度量学习计划(UDML),它不仅利用视觉和文本内容的社会图像,而且有效地结合归纳和转换度量学习技术系统的学习框架。进一步提出了一种求解UDML优化问题的随机梯度下降算法，并证明了该算法的收敛性。通过将该技术应用于图像自动标注任务，证明了该技术在实际应用中具有良好的经验效果和应用前景。
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| 课程简介: |
With the popularity of various social media applications, massive social images associated with high quality tags have been made available in many social media web sites nowadays. Mining social images on the web has become an emerging important research topic in web search and data mining. In this paper, we propose a machine learning framework for mining social images and investigate its application to automated image tagging. To effectively discover knowledge from social images that are often associated with multimodal contents (including visual images and textual tags), we propose a novel Unified Distance Metric Learning (UDML) scheme, which not only exploits both visual and textual contents of social images, but also effectively unifies both inductive and transductive metric learning techniques in a systematic learning framework. We further develop an efficient stochastic gradient descent algorithm for solving the UDML optimization task and prove the convergence of the algorithm. By applying the proposed technique to the automated image tagging task in our experiments, we demonstrate that our technique is empirically effective and promising for mining social images towards some real applications.
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| 关 键 词: |
计算机科学; Web挖掘; 机器学习
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| 课程来源: |
视频讲座网
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| 最后编审: |
2020-06-08：heyf
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| 阅读次数: |
37
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