Detecting tourist attractions using geo-tagged photo clustering

Millions of geo-tagged photos are becoming available due to the wide spread of photo-sharing websites, which provide valuable information to mine spatial patterns from human activities. In this study, we present a simple and fast density-based spatial clustering algorithm to detect popular scenic spots using geo-tagged photos collected from Flickr. In this algorithm, Gaussian kernel is applied to estimate local density of data points, and a decision graph is used to obtain cluster centers easily. More than 289,000 geo-tagged photos located in five typical cities of China are downloaded as case studies, and data pre-processing such as duplicate removing is performed to improve the quality of clustering result. Finally, popular tourist attractions of each sample city are successfully detected with this algorithm, and our result is useful for recommending some interesting destinations which might not be on the list of tourist website or mobile guide applications. The proposed solution is robust with respect to different distributions of photos, and it is efficient by comparing with other popular clustering approaches.

Automatic Landmark Detecting by Flickr Photo Clustering

In this paper, we concentrate on how to automatic detect landmarks of a city leveraging the community-contributed collections of rich media on the Web, as landmark for a given city could provide helpful information for tourist guides. Our approach only need the user to provide the city name, and then submit it to Flickr website to obtain photos and related metadata. Next, these Flickr photos are clustered by simultaneously integrating multiple types of metadata which are related to Flickr photos. Finally, landmarks are mined from the photos clustering results. Experiments conducted on the photos in Flickr demonstrate the effectiveness of the proposed approach and our approach could enhance the performance of tourist guiding systems greatly.

An Efficient Graph-Based Flickr Photo Clustering Algorithm

Traditional image clustering methods mainly depends on visual features only. Due to the well-known “semantic gap”, visual features can hardly describe the semantics of the images independently. In the case of Web images, apart from visual features, there are rich metadata which could enhance the performance of image clustering, such as time information, GPS coordinate and initial annotations. This paper proposes an efficient Flickr photo clustering algorithm by simultaneous integration information of multiple types which are related to Flickr photos using k-partite graph partitioning. For a personal collection of Flickr, we firstly determine the value of k which means the number of data types we used. Secondly, these heterogeneous metadata are mapped to vertices of a k-partite graph, and relationship between the heterogeneous metadata is represented as edge weight. Finally, Flickr photos could be clustered by partitioning the k-partite graph. Experiments conducted on the photos in Flickr demonstrate the effectiveness of the proposed algorithm.