What Makes Paris Look Like Paris?
What Makes Paris Look Like Paris?
复制标题
DOI：
10.1145/2830541
复制
发表时间：
2015-12-01
影响因子：
22.7
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.
给定一个地理标记图像的大型存储库，我们试图自动找到视觉元素，例如窗户，阳台和街道标志，这些元素对于特定的地理空间区域（例如巴黎）来说是最有特色的。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，我们还面临着一个难以搜索的问题：给定所有图像中所有可能的补丁，其中哪些是频繁出现的，哪些是地理信息丰富的？为了解决这些问题，我们建议使用一个歧视性的聚类方法，能够考虑到薄弱的地理监督。我们表明，地理上具有代表性的图像元素，可以发现自动从谷歌街景图像中的歧视性的方式。我们证明，这些元素是视觉上可解释的和感知的地理信息。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 geo-tagged imagery, we seek to automatically find visual elements, for example 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.