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归一化切口的凸转导

Convex transduction with the normalized cut
课程网址: http://videolectures.net/mlsvmlso05_bie_ctnc/  
主讲教师: Tijl De Bie
开课单位: 布里斯托尔大学
开课时间: 2007-02-25
课程语种: 英语
中文简介:
我们讨论了基于图切割成本函数的转换方法。更具体地说,我们关注归一化切割,这是许多聚类应用中的成本函数选择,特别是在图像分割中。由于优化归一化切割成本是一个NP完全问题,很多到目前为止,研究关注的重点是放松归一化切割聚类到易处理问题的问题,产生光谱弛豫,最近更严格但计算上更严格的半定规划(SDP)松弛。在本文中,我们提供了两个主要的贡献:首先,我们展示了替代的SD松弛如何产生更容易处理的优化问题,并且我们展示了如何通过进行原理近似来进一步提高可伸缩性和速度。其次,我们展示了如何使用新提出的方法有效地优化转换设置中的标准化切割成本。报告了积极的实证结果。
课程简介: We discuss approaches to transduction based on graph cut cost functions. More specifically, we focus on the normalized cut, which is the cost function of choice in many clustering applications, notably in image segmentation. Since optimizing the normalized cut cost is an NP-complete problem, much of the research attention so far has gone to relaxing the problem of normalized cut clustering to tractable problems, producing so far a spectral relaxation and a more recently a tighter but computationally much tougher semi-definite programming (SDP) relaxation. In this paper we deliver two main contributions: first, we show how an alternative SDP relaxation yields a much more tractable optimization problem, and we show how scalability and speed can further be increased by making a principled approximation. Second, we show how it is possible to efficiently optimize the normalized cut cost in a transduction setting using our newly proposed approaches. Positive empirical results are reported.
关 键 词: 图切割; 成本函数; 归一化切割
课程来源: 视频讲座网
最后编审: 2019-07-23:cwx
阅读次数: 48