What Makes Paris Look like Paris?
What Makes Paris Look like Paris?
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DOI：
10.1145/2185520.2185597
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发表时间：
2012-07-01
影响因子：
6.2
通讯作者：
Efros, Alexei A.
中科院分区：
文献类型：
--
作者：
Doersch, Carl;Singh, Saurabh;Efros, Alexei A.
给定一个大的地理标记图像库，我们寻求自动找到视觉元素，例如窗户、阳台和街道标志，这些元素在特定的地理空间区域（例如巴黎城市）中最具特色。这是一项非常困难的任务，因为区分不同地方建筑元素的视觉特征可能非常微妙。此外，我们还面临一个难搜索问题：给定所有图像中所有可能的斑块，其中哪些是频繁出现且地理信息丰富的？为了解决这些问题，我们建议使用一种能够考虑到弱地理监督的判别聚类方法。我们证明了地理上具有代表性的图像元素可以以判别的方式从谷歌街景图像中自动发现。我们证明这些元素在视觉上是可解释的，在感知上是地理信息的。发现的视觉元素还可以支持各种计算地理任务，例如绘制城市内部和城市之间的建筑对应关系和影响，寻找不同地理空间尺度的代表性元素，以及地理信息图像检索。
Given a large repository of geotagged imagery, we seek to automatically find visual elements, e. g. windows, balconies, and street signs, that are most distinctive for a certain geo-spatial area, for example the city of Paris. This is a tremendously difficult task as the visual features distinguishing architectural elements of different places can be very subtle. In addition, we face a hard search problem: given all possible patches in all images, which of them are both frequently occurring and geographically informative? To address these issues, we propose to use a discriminative clustering approach able to take into account the weak geographic supervision. We show that geographically representative image elements can be discovered automatically from Google Street View imagery in a discriminative manner. We demonstrate that these elements are visually interpretable and perceptually geo-informative. The discovered visual elements can also support a variety of computational geography tasks, such as mapping architectural correspondences and influences within and across cities, finding representative elements at different geo-spatial scales, and geographically-informed image retrieval.