Abstract
Detecting events from web resources has attracted increasing research interests in recent years. Flickr is one of Web resources, which is used to share photos. Complex event detection on Flickr includes the detection of tourist features, user’s interest, and so on. With the increasing user requirements of efficient and personalized services, the detection of scene features in Flickr is urgently needed. In this paper we propose a novel method to detect tourist features of every scene, and its difference in different seasons as a probabilistic combination of tags. The use of topic models enables the automatic detection of such patterns, which can translate unstructured tag information into structured event form. The experimental evaluation using real datasets in Flickr show the feasibility and efficiency of the proposed method.
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References
Allan, J., Carbonell, J.G., Doddington, G., Yamron, J., Yang, Y.: Topic Detection and Tracking Pilot Study: Final Report. In: DARPA Broadcast News Transcription and Understanding Workshop (1998)
Yang, Y., Pierce, T., Carbonell, J.G.: A Study of Retrospective and On-line Event Detection. In: The 21th Annual International ACM SIGIR Conference (SIGIR), pp. 28–36 (1998)
He, Q., Chang, K., Lim, E.P.: Analyzing Feature Trajectories for Event Detection. In: The 30th Annual International ACM SIGIR Conference (SIGIR), pp. 207–214 (2007)
Li, Z., Wang, B., Li, M., Ma, W.Y.: A Probabilistic Model for Retrospective News Event Detection. In: The 28th Annual International ACM SIGIR Conference, SIGIR (2005)
Kleinberg, J.M.: Bursty and Hierarchical Structure in Streams. In: The 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), vol. 7(4), pp. 373–397 (2003)
Halpin, H., Robu, V., Shepherd, H.: The Complex Dynamics fo Collaborative Tagging. In: The 16th International Conference on World Wide Web (WWW), pp. 211–220 (2007)
Dubinko, M., Kumar, J., Magnani, J., et al.: Visualizing Tags over Time. In: The 15th International Conference on World Wide Web (WWW), pp. 193–202 (2006)
Dmitriev, P.A., Eiron, N., Fontoura, M., Shekita, E.: Using Annotations in Enterprise Search. In: The 15th International Conference on World Wide Web (WWW), pp. 811–817 (2006)
Bao, S., Xue, G.R., Wu, X., Yu, Y., Su, Z.: Optimizing Web Search Using Social Annotations. In: The 16th International Conference on World Wide Web (WWW), pp. 501–510 (2007)
Rattenbury, T., Good, N., Naaman, M.: Towards Automatic Extraction of Event and Place Semantics from Flickr Tags. In: The 30th Annual International ACM SIGIR Conference (SIGIR), pp. 103–110 (2007)
Popescu, A., Grefenstette, G., Moellic, P.A.: Mining Tourist Information from User-Supplied Collections. In: The 18th ACM Conference on Information and Knowledge Management (CIKM), pp. 1713–1716 (2009)
Rattenbury, T., Good, N., Naaman, M.: Towards Automatic Extraction of Event and Place Semantics from Flickr Tags. In: Proceedings of the 30th Annual International ACM SIGIR Conference (2007)
Ahern, S., Naaman, M., Nair, R., Yang, J.: World Explorer: Visualizing Aggregate Data from Unstructured Text in Georeferenced Collections. In: Proceedings of the ACM IEEE Joint Conference on Digital Libraries, JCDL (2007)
Quack, T., Leibe, B., van Gool, L.: World-Scale Mining of Objects and Events from Community Photo Collections. In: Proceedings of the 7th ACM International Conference on Image and Video Retrieval, CIVR (2008)
Crandall, D., Backstrom, L., Hutternlocher, D., Kleinberg, J.: Mapping the World’s photos. In: Proceedings of the 18th International World Wide Web Conference, WWW (2009)
Zheng, I., Zhang, L., Xie, X., Ma, W.Y.: Mining Interesting Locations and Travel Sequences from GPS Trajectories. In: Proceedings of the 18th International World Wide Web Conference, WWW (2009)
Gonotti, F., et al.: Trajectory Pattern Mining. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 330–339 (2007)
Girardin, F., Dal, F., Blat, J., et al.: Understanding of Tourist Dynamics from Explicitly Disclosed Location Information. In: Proceedings of the 4th International Symposium on LBS and Telecartography (2007)
Chen, Z., Shen, H.T., Zhou, X., Zheng, Y., Xie, X.: Searching Trajectories by Locations-An Efficiency Study. In: Proceedings of the 36th SIGMOD International Conference on Management of Data, SIGMOD (2010)
Home and Abroad, http://homeandabroad.com
Popescu, A., Grefenstette, G.: Deducing Trip Related Information from Flickr. In: Proceedings of the 18th International World Wide Web Conference, WWW (2009)
Popescu, A., Grefenstette, G., Alain, P.: Mining Tourist Information from User-Supplied Collections. In: Proceedings of The 18th ACM Conference on Information and Knowledge Management, CIKM (2009); SIGMOD (2004)
Shekhar, S., Liu, D.: CCAM: A Connectivity Clustered Acccess Method for Networks and Network Computations. IEEE Transactions on Knowledge and Data Engineering (TKDE), 102–119 (1997)
Li, F., Cheng, D.: On trip planning queries in spatial databases. In: Anshelevich, E., Egenhofer, M.J., Hwang, J. (eds.) SSTD 2005. LNCS, vol. 3633, pp. 273–290. Springer, Heidelberg (2005)
Blei, D., Ng, A., Jordan, M.: Latent Dirichlet Allocation. The Journal of Machine Learning Research, 993–1022 (2003)
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Zhou, C., Dai, P., Liu, J. (2011). The Detection of Scene Features in Flickr. In: Luo, X., Cao, Y., Yang, B., Liu, J., Ye, F. (eds) New Horizons in Web-Based Learning - ICWL 2010 Workshops. ICWL 2010. Lecture Notes in Computer Science, vol 6537. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20539-2_24
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DOI: https://doi.org/10.1007/978-3-642-20539-2_24
Publisher Name: Springer, Berlin, Heidelberg
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