Tour the world: Building a web-scale landmark recognition engine
Tour the world: Building a web-scale landmark recognition engine
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DOI：
10.1109/cvpr.2009.5206749
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发表时间：
2009-06
期刊：
影响因子：
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通讯作者：
Yantao Zheng;Ming Zhao;Yang Song;Hartwig Adam;Ulrich Buddemeier;A. Bissacco;Fernando Brucher;
中科院分区：
文献类型：
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作者：
Yantao Zheng;Ming Zhao;Yang Song;Hartwig Adam;Ulrich Buddemeier;A. Bissacco;Fernando Brucher;
在世界尺度上建模和识别地标是一项有用但具有挑战性的任务。目前还没有现成的世界地标名单。为每个地标获取可靠的视觉模型也会带来问题，对于如此大规模的系统来说，效率是另一个挑战。本文利用网络上大量的多媒体数据、互联网图像搜索引擎的可用性以及目标识别和聚类技术的进步来解决这些问题。首先，从两个来源中挖掘出一个全面的地标列表：(1)约2000万张gps标记的照片和(2)在线导游网页。然后从照片共享网站或通过查询图像搜索引擎获得每个地标的候选图像。其次，利用高效的图像匹配和无监督聚类技术对候选图像进行剪枝，建立地标视觉模型；最后，通过检查其成员图像的作者身份来验证地标及其视觉模型。由此产生的地标识别引擎包含来自144个国家1259个城市的5312个地标。实验结果表明，该引擎具有较高的识别效率和较好的识别性能。
Modeling and recognizing landmarks at world-scale is a useful yet challenging task. There exists no readily available list of worldwide landmarks. Obtaining reliable visual models for each landmark can also pose problems, and efficiency is another challenge for such a large scale system. This paper leverages the vast amount of multimedia data on the Web, the availability of an Internet image search engine, and advances in object recognition and clustering techniques, to address these issues. First, a comprehensive list of landmarks is mined from two sources: (1) ~20 million GPS-tagged photos and (2) online tour guide Web pages. Candidate images for each landmark are then obtained from photo sharing Websites or by querying an image search engine. Second, landmark visual models are built by pruning candidate images using efficient image matching and unsupervised clustering techniques. Finally, the landmarks and their visual models are validated by checking authorship of their member images. The resulting landmark recognition engine incorporates 5312 landmarks from 1259 cities in 144 countries. The experiments demonstrate that the engine can deliver satisfactory recognition performance with high efficiency.