Mining Flickr Landmarks by Modeling Reconstruction Sparsity
Mining Flickr Landmarks by Modeling Reconstruction Sparsity
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DOI：
10.1145/2037676.2037688
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发表时间：
2011-10-01
影响因子：
5.1
通讯作者：
Tian, Qi
中科院分区：
文献类型：
--
作者：
Ji, Rongrong;Gao, Yue;Tian, Qi
近年来，在Flickr等社区网站上出现了越来越多的地理标记照片。从这些照片中发现旅游地标可以帮助我们更好地理解我们的视觉世界。在这篇文章中，我们报告了我们的工作，从带地理标记的Flickr照片中挖掘地标，用于城市场景摘要和旅游推荐。我们从探索Web用户拍照方式的地理和视觉统计入手，在此基础上分两步进行地标挖掘：首先，提出了基于Flickr照片地理标签的光谱聚类将每个城市划分为地理区域。其次，在每个标志性区域中，提出了一种基于稀疏表示的具有代表性的照片挖掘方案。我们的主要思想是将地标挖掘问题看作一个过程，即寻找可以利用该地标区域的其他照片以最小编码长度重建视觉特征的照片的过程。这种稀疏重建方案为挖掘具有代表性的照片提供了一个通用的视角。事实上，通过简化方案中的数据相关性约束，可以派生出以前在代表性照片发现和地标挖掘方面的几项工作。最后，我们引入了一个超链接诱导的主题搜索模型来改进我们的地标排名，该模型结合了社区知识，将地标排名问题模拟为一个动态页面排名问题。我们已经在城市场景摘要和导航系统上部署了我们提出的具有里程碑意义的挖掘框架，该系统处理来自全球20个大都市的100万张带地理标记的Flickr照片。我们还将我们的方案与几个最先进的作品进行了定量比较。
In recent years, there have been ever-growing geographical tagged photos on the community Web sites such as Flickr. Discovering touristic landmarks from these photos can help us to make better sense of our visual world. In this article, we report our work on mining landmarks from geotagged Flickr photos for city scene summarization and touristic recommendations. We begin by exploring the geographical and visual statistics of the Web users' photographing manner, based on which we conduct landmark mining in two steps: First, we propose to partition each city into geographical regions based on spectral clustering over the geotags of Flickr photos. Second, in each landmark region, we present a representative photo mining scheme based on sparse representation. Our main idea is to regard the landmark mining problem as a process to find photos whose visual signatures can be reconstructed using other photos of this landmark region with a minimal coding length. This sparse reconstruction scheme offers a general perspective to mine the representative photos. Indeed, by simplifying the data correlation constraints in our scheme, several previous works in representative photo discovery and landmark mining can be derived. Finally, we introduce a Hyperlink-Induced Topic Search model to refine our landmark ranking, which incorporates the community knowledge to simulate the landmark ranking problem as a dynamic page ranking problem. We have deployed our proposed landmark mining framework on a city scene summarization and navigation system, which works on one million geotagged Flickr photos coming from twenty worldwide metropolises. We have also quantitatively compared our scheme with several state-of-the-art works.