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大型地标图像集合的几何验证的改进

Improved Geometric Verification for Large Scale Landmark Image Collections
课程网址: http://videolectures.net/bmvc2012_tighe_image_collections/  
主讲教师: Joseph Tighe
开课单位: 北卡罗来纳大学
开课时间: 2012-10-09
课程语种: 英语
中文简介:
在这项工作中,我们解决了几何验证问题,重点是对从互联网收集的大型地标图像集进行建模。特别是,我们表明,我们可以通过利用作为匹配和验证阶段的副产品而产生的信息来计算和学习与图像集合相关的描述性统计。我们的方法基于以下直觉:验证相同几何场景结构的多个图像对可以快速揭示关于图像集合的两个方面的有用信息:(a)单个视觉单词的可靠性和(b)图像中地标的外观采集。然后可以使用这两种信息源来驱动任何后续处理,从而允许系统自我引导。虽然目前的技术使用专门的培训/预处理阶段,但我们的方法通过简单地利用验证阶段显示的信息,优雅地集成到标准几何验证管道中。这项工作的主要成果是,这种无人监督的“随时随地学习”。方法显着提高了性能;我们的实验表明,与标准技术相比,效率和完整性有了显着提高
课程简介: In this work, we address the issue of geometric verification, with a focus on modeling large-scale landmark image collections gathered from the internet. In particular, we show that we can compute and learn descriptive statistics pertaining to the image collection by leveraging information that arises as a by-product of the matching and verification stages. Our approach is based on the intuition that validating numerous image pairs of the same geometric scene structures quickly reveals useful information about two aspects of the image collection: (a) the reliability of individual visual words and (b) the appearance of landmarks in the image collection. Both of these sources of information can then be used to drive any subsequent processing, thus allowing the system to bootstrap itself. While current techniques make use of dedicated training/preprocessing stages, our approach elegantly integrates into the standard geometric verification pipeline, by simply leveraging the information revealed during the verification stage. The main result of this work is that this unsupervised “learning-as-you-go” approach significantly improves performance; our experiments demonstrate significant improvements in efficiency and completeness over standard techniques.
关 键 词: 几何验证; 图像采集; 快速采集
课程来源: 视频讲座网
最后编审: 2020-06-29:yumf
阅读次数: 72