Mining flickr landmarks by modeling reconstruction sparsity

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.