Journal of Software, Vol 7, No 6 (2012), 1393-1402, Jun 2012
doi:10.4304/jsw.7.6.1393-1402

Analyzing Effective Features based on User Intention for Enhanced Map Search

Junki Matsuo, Daisuke Kitayama, Ryong Lee, Kazutoshi Sumiya

Abstract


Map would be the most critical information in daily real-world activities. Due to the advance of the Web and digital map processing techniques, we can now easily find various maps of different presentations appropriate to diverse user purposes such as trivial searching for a restaurant or consulting a path during a trip. However, maps served by today’s representative map search engines such as Google Maps cannot satisfy all users whose map-reading ability and search purposes are quite different. Thus, map search engine need to provide maps well represented for specific needs. Nowadays, there are numerous numbers of map contents available on the web, which are appropriately well drawn and shared on various web sites. However, it is not an easy task for users to find out appropriate maps on the Web. In order to support user's map search on the Web, we developed a map search system, which can search for map contents drawn in various viewpoints by interacting with users based on a relevance feedback. In particular, we analyze each map content according to two distinguishing features, geographical features and image features. Significantly, the proposed system can deal with visual map contents by considering how the map contents are represented. In this paper, we analyze effective features based on user intention for map search.


Keywords


map; search engine; user intention

References


 

[1] Google maps, http://maps.google.com/.

[2] Bing maps, http://www.bing.com/maps/.

[3] K. Kobayashi, R. Lee, and K. Sumiya, Systematic measurement of human map-reading ability with street-view based navigation systems, in Proc. of 4th International Conference on Ubiquitous Information Management and Communication (ICUIMC 2010), 2010, pp. 286-293.

[4] C. Cortes and V. Vapnik, Support-vector networks, Machine Learning, vol. 20, no. 3, pp. 273-297, 1995.
http://dx.doi.org/10.1007/BF00994018

[5] H. Honda, K. Yamamori, K. Kajita, and J. Hasegawa, A system for automated generation of deformed maps, in Proc. of the IAPR Workshop on Machine Vision Applications (MVA 1998), 1998, pp. 149-153.

[6] K. Fujii and K. Sugiyama, Route guide map generation system for mobile communication, Transactions of Information Processing Society of Japan, vol. 41, no. 9, pp. 2394-2403, 2000.

[7] M. Agrawala and C. Stolte, Rendering effective route maps: Improving usability through generalization, in Proc. of 28th Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH 2001), 2001, pp. 241-249.
http://dx.doi.org/10.1145/383259.383286

[8] T. Osaragi and S. Onozuka, Map element extraction model for pedestrian route guidance map, in Proc. of 4th IEEE International Conference on Cognitive Informatics (ICCI 2005), 2005, pp. 144-153.
http://dx.doi.org/10.1109/COGINF.2005.1532626

[9] F. Grabler, M. Agrawala, R. W. Sumner, and M. Pauly, Automatic generation of tourist maps, in Proc. of 35th International Conference on Computer Graphics and Interactive Techniques (SIGGRAPH 2008), 2008, pp. 1-11.

[10]M. Michelson, A. Goel, and C. A. Knoblock, Identifying maps on the world wide web, in Proc. of 5th International Conference on Geographic Information Science, 2008, pp. 249-260.

[11]Y. Y. Chiang and C. A. Knoblock, Classification of raster maps for automatic feature extraction, in Proc. of 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2009, pp. 138-147.

[12]S. Newsam, D. Leung, O. Caballero, J. Floreza, and J. Pulido, Cbgir: content-based geographic image retrieval, in Proc. of 18th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2010, pp. 526-527.

[13]K. Oku, S. Nakajima, J. Miyazaki, S. Uemura, and H. Kato, A ranking method based on users' contexts for information recommendation, in Proc. of 2nd International Conference on Ubiquitous Information Management and Communication (ICUIMC 2008), 2008, pp. 289-295.

[14]K. Lynch, The Image of the City. The MIT Press, 1960.

[15]K. Oku, S. Nakajima, J. Miyazaki, and S. Uemura, Context-aware recommendation system based on contextdependent user preference modeling, IPSJ Transactions on Databases, vol. 48, no. 11, pp. 162-176, 2007.

[16]Libsvm - a library for support vector machines, http://www.csie.ntu.edu.tw/?cjlin/libsvm/.

[17]D. Kitayama, R. Lee, and K. Sumiya, Deformation analysis based on geographical accuracy and spatial context for modified maps credibility, in Proc. of 44th Hawaii International Conference on System Sciences (HICSS-44), 2011, pp. 1-9.


Full Text: PDF


Journal of Software (JSW, ISSN 1796-217X)

Copyright @ 2006-2013 by ACADEMY PUBLISHER – All rights reserved.