Yue Gao
ABSTRACT
In this paper, we present a travel guidance system W2Go (Where to Go), which can automatically recognize and rank the landmarks for travellers. In this system, a novel Automatic Landmark Ranking (ALR) method is proposed by utilizing the tag and geo-tag information of photos in Flickr and user knowledge from Yahoo Travel Guide. ALR selects the popular tourist attractions (landmarks) based on not only the subjective opinion of the travel editors as is currently done on sites like WikiTravel and Yahoo Travel Guide, but also the ranking derived from popularity among tourists. Our approach utilizes geo-tag information to locate the positions of the tag-indicated places, and computes the probability of a tag being a landmark/site name. For potential landmarks, impact factors are calculated from the frequency of tags, user numbers in Flickr, and user knowledge in Yahoo Travel Guide. These tags are then ranked based on the impact factors. Several representative views for popular landmarks are generated from the crawled images with geo-tags to describe and present them in context of information derived from several relevant reference sources. The experimental comparisons to the other systems are conducted on eight famous cities over the world. User-based evaluation demonstrates the effectiveness of the proposed ALR method and the W2Go system.
- Yahoo travel guide. http://travel.yahoo.com/. website.Google Scholar
- R. Abbasi, S. Chernov, W. Nejd, R. Paiu, and S. Staab. Exploiting flickr tags and groups for finding landmark photos. ECIR, 2009. Google ScholarDigital Library
- S. Ahern, M. Naaman, R. Nair, and J. H.-I. Yang. World explorer: visualizing aggregate data from unstructured text in geo-referenced collections. JCDL, 2007. Google ScholarDigital Library
- D.J. Crandall, L. Backstrom, D. Huttenlocher and J. Kleinberg. Mapping the world's photos. WWW, 2009. Google ScholarDigital Library
- Flickr. http://www.flickr.com/. website.Google Scholar
- Flickr. www.flickr.com/services/api.Google Scholar
- R. Hong, J. Tang, Z. Zha, Z. Luo, and T.S. Chua Mediapedia: Mining Web Knowledge to Construct Multimedia Encyclopedia. MMM, 2010. Google ScholarDigital Library
- L. S. Kennedy and M. Naaman. Generating diverse and representative image search results for landmarks. WWW, 2008. Google ScholarDigital Library
- H. Kretzschmar, C. Stachniss, C. Plagemann, and W. Burgard. Estimating landmark locations from geo-referenced photographs. ICIRS, 2008.Google ScholarCross Ref
- S. Kullback and R. Leibler. On information and sufficiency. Annals of Mathematical Statistics, 22(1):79--86, 1951.Google ScholarCross Ref
- X. Li, L. Guo, and Y. Zhao. Tag-based social interest discovery. WWW, 2008. Google ScholarDigital Library
- D. Liu, X. Hua, L. Yang, and M. Wang, and H. Zhang. Tag ranking. WWW, 2009. Google ScholarDigital Library
- D. G. Lowe. Distinctive image features from scale-invariant keypoints. IJCV, 60(2):91--110, 2004. Google ScholarDigital Library
- E. Moxley, J. Kleban, and B. Manjunath. Spirittagger: a geo-aware tag suggestion tool mined from flickr. MIR, 2008. Google ScholarDigital Library
- E. Moxley, J. Kleban, J. Xu, and B. Manjunath. Not all tags are created equal: Learning flickr tag semantics for global annotation. ICME, 2009. Google ScholarDigital Library
- Panoramio. http://www.panoramio.com/. website.Google Scholar
- T. Rattenbury, N. Good, and M. Naaman. Towards automatic extraction of event and place semantics from flickr tags. SIGIR, 2007. Google ScholarDigital Library
- T. Rattenbury, N. Good, and M. Naaman. Towards extracting flickr tag semantics. WWW, 2007. Google ScholarDigital Library
- J. Shi and J. Malik. Normalized cuts and image segmentation. IEEE TPAMI, 22(8):888--905, 2000. Google ScholarDigital Library
- J. Tang, S. Yan, R. Hong, G. Qi, and T. Chua. Inferring Semantic Concepts from Community-Contributed Images and Noisy Tags. MM, 2009. Google ScholarDigital Library
- M. Wang, K. Yang, X.S. Hua, and H.J. Zhang. Towards Relevant and Diverse Search of Social Images. IEEE Transactions on Multimedia, to appear. Google ScholarDigital Library
- M. Wang, B. Liu, and X.S. Hua. Accessible Image Search for Colorblindness. ACM Transactions on Intelligent Systems and Technology, to appear. Google ScholarDigital Library
- WikiTravel. http://wikitravel.org/. website.Google Scholar
- K. Yanai, H. Kawakubo, and B. Qiu. A visual analysis of the relationship between word concepts and geographical locations. CIVR, 2009. Google ScholarDigital Library
- YouTube. http://www.youtube.com. website.Google Scholar
- Z. Zha, L. Yang, T. Mei, M. Wang, and Z. Wang. Visual Query Suggestion. MM, 2009. Google ScholarDigital Library
- Y. Zheng, M. Zhao, Y. Song, H. Adam, U. Buddemeier, A. Bissacco, F. Brucher, T. Chua, and H. Neven. Tour the world: building a web-scale landmark recogntion engine. CVPR, 2009.Google ScholarCross Ref
Index Terms
- W2Go: a travel guidance system by automatic landmark ranking
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