Discovering favorite views of popular places with iconoid shift

Tobias Weyand, B. Leibe
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引用次数: 51

Abstract

In this paper, we propose a novel algorithm for automatic landmark building discovery in large, unstructured image collections. In contrast to other approaches which aim at a hard clustering, we regard the task as a mode estimation problem. Our algorithm searches for local attractors in the image distribution that have a maximal mutual homography overlap with the images in their neighborhood. Those attractors correspond to central, iconic views of single objects or buildings, which we efficiently extract using a medoid shift search with a novel distance measure. We propose efficient algorithms for performing this search. Most importantly, our approach performs only an efficient local exploration of the matching graph that makes it applicable for large-scale analysis of photo collections. We show experimental results validating our approach on a dataset of 500k images of the inner city of Paris.
发现最喜欢的观点,受欢迎的地方与象形变换
在本文中,我们提出了一种在大型非结构化图像集合中自动发现地标建筑的新算法。与其他以硬聚类为目标的方法不同,我们将该任务视为一个模式估计问题。我们的算法在图像分布中搜索与其邻域图像具有最大互单应性重叠的局部吸引子。这些吸引子对应于单个物体或建筑物的中心、标志性视图,我们使用带有新距离度量的介质移位搜索有效地提取这些视图。我们提出了执行这种搜索的有效算法。最重要的是,我们的方法只对匹配图进行有效的局部探索,这使得它适用于照片集的大规模分析。我们展示了实验结果,验证了我们在巴黎内城50万张图像数据集上的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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