TY - GEN
T1 - Learning the trip suggestion from landmark photos on the web
AU - Ji, Rongrong
AU - Duan, Ling Yu
AU - Chen, Jie
AU - Yang, Shuang
AU - Yao, Hongxun
AU - Huang, Tiejun
AU - Gao, Wen
PY - 2011
Y1 - 2011
N2 - In this paper, we introduce a novel touristic trip suggestion system to facilitate the traveling of mobile users in a given city. Given the current user location and his touristic destination, our system can suggest a shortest trip path that visits as many popular landmarks as possible. To this end, we collect geographical tagged photos from Flickr [1] and Panoramio [2] photo sharing websites. Then a geographical graph is constructed by modeling photos as vertices and their geographical and visual closenesses as connection strengths. In this graph, we mine a dominant subgraph by quantizing nearby and visually duplicated vertices, and then trimming unpopular subgraphs. Such dominant subgraph only retains the popular landmarks from the consensus of travelers in this city. In online suggestion, we map the current user location and the target location to the nearest vertices in this subgraph, based on which an optimal trip is suggested through a shortest path search. We have quantitatively validated our system in typical areas including Beijing and New York City, with quantitative comparisons to alternative approaches.
AB - In this paper, we introduce a novel touristic trip suggestion system to facilitate the traveling of mobile users in a given city. Given the current user location and his touristic destination, our system can suggest a shortest trip path that visits as many popular landmarks as possible. To this end, we collect geographical tagged photos from Flickr [1] and Panoramio [2] photo sharing websites. Then a geographical graph is constructed by modeling photos as vertices and their geographical and visual closenesses as connection strengths. In this graph, we mine a dominant subgraph by quantizing nearby and visually duplicated vertices, and then trimming unpopular subgraphs. Such dominant subgraph only retains the popular landmarks from the consensus of travelers in this city. In online suggestion, we map the current user location and the target location to the nearest vertices in this subgraph, based on which an optimal trip is suggested through a shortest path search. We have quantitatively validated our system in typical areas including Beijing and New York City, with quantitative comparisons to alternative approaches.
KW - graph quantization
KW - shortest path
KW - social media
KW - tourist recommendation
KW - trip suggestion
UR - http://www.scopus.com/inward/record.url?scp=84863049775&partnerID=8YFLogxK
U2 - 10.1109/ICIP.2011.6116165
DO - 10.1109/ICIP.2011.6116165
M3 - Conference contribution
AN - SCOPUS:84863049775
SN - 9781457713033
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 2485
EP - 2488
BT - ICIP 2011
T2 - 2011 18th IEEE International Conference on Image Processing, ICIP 2011
Y2 - 11 September 2011 through 14 September 2011
ER -