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首页 >> 新闻公告 >> 学术报告 >> 6月16日(周五)上午10:00天津大学朱鹏飞副教授学术报告 --黎曼流形上的度量学习
6月16日(周五)上午10:00天津大学朱鹏飞副教授学术报告 --黎曼流形上的度量学习
2017-06-14 13:00

616日(周五)上午10:00天津大学朱鹏飞副教授学术报告

--黎曼流形上的度量学习

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报告题目:黎曼流形上的度量学习

人:朱鹏飞 副教授

    位:天津大学

    间:2017616日周五上午10:00

    点:中心楼428

邀请人/主持人:杨万扣 副研究员

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欢迎各位老师和研究生参加!

 

报告摘要:

Modeling images or videos on Riemannian manifold has been successfully used in a variety of computer vision tasks, including image set classification, object tracking, and motion segmentation, etc. Because of the intrinsic geometry structure inherent in Riemannian manifold, the geodesic distance metrics are necessary for matching on Riemannian manifold. Although numerous metric learning methods on Riemannian manifold have been proposed, they can only apply to a specific manifold (e.g., symmetric positive definite (SPD) manifold or Grassmann manifold). In this paper, we propose a unified metric learning framework for Riemannian manifold (RMML). RMML learns a discriminative metric in the tangent space of Riemannian manifold or the original Grassmann manifold. Specifically, the metric learning problem is formulated as solving the geodesic distance between two SPD matrices, one generated from similar manifold pairs and the other from dissimilar ones. RMML can be applied to both SPD manifold and Grassmann manifold. What's more, the proposed RMML can perform very efficiently with a closed-form solution. Experimental results on image set classification, video based face recognition, and material classification show that RMML achieves superior performance in terms of both accuracy and efficiency.

 

报告人简介:

朱鹏飞,2015年于香港理工大学电子计算学系获得博士学位。随后到天津大学计算机科学与技术学院工作,任副教授和硕士生导师。主要研究方向是机器学习和计算机视觉。目前主持国家自然科学基金青年项目1项,天津市自然科学基金重点项目1项。在机器学习与计算机视觉相关国际会议和期刊上发表论文30余篇,包括ICCVCVPRECCVAAAIIJCAI,以及IEEE TIFSTYCTIP等。近几年,担任IJCRS 2015 CCML 2017CCCV 2017本地组织主席,国际期刊International Journal of Image and Graphics编委。获得黑龙江省高校自然科学一等奖(排名第5)和黑龙江省自然科学一等奖(排名第5)。


 

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