| Generating location overviews with images and tags by mining user-generated travelogues |
| Full text |
Pdf
(1.78 MB)
|
Source
|
International Multimedia Conference
archive
Proceedings of the seventeen ACM international conference on Multimedia
table of contents
Beijing, China
SESSION: Short papers session 3: applications and systems
table of contents
Pages 801-804
Year of Publication: 2009
ISBN:978-1-60558-608-3
|
|
Authors
|
|
Qiang Hao
|
Tianjin University, Tianjin, China
|
|
Rui Cai
|
Microsoft Research Asia, Beijing, China
|
|
Xin-Jing Wang
|
Microsoft Research Asia, Beijing, China
|
|
Jiang-Ming Yang
|
Microsoft Research Asia, Beijing, China
|
|
Yanwei Pang
|
Tianjin University, Tianjin, China
|
|
Lei Zhang
|
Microsoft Research Asia, Beijing, China
|
|
| Sponsor |
|
| Publisher |
|
| Bibliometrics |
Downloads (6 Weeks): 15, Downloads (12 Months): 15, Citation Count: 0
|
|
|
ABSTRACT
Automatically generating location overviews in the form of both visual and textual descriptions is highly desired for online services such as travel planning, to provide attractive and comprehensive outlines of travel destinations. Actually, user-generated content (e.g., travelogues) on the Web provides abundant information to various aspects (e.g., landmarks, styles, activities) of most locations in the world. To leverage the experience shared by Web users, in this paper we propose a location overview generation approach, which first mines location-representative tags from travelogues and then uses such tags to retrieve web images. The learnt tags and retrieved images are finally presented via a novel user interface which provides an informative overview for a given location. Experimental results based on 23,756 travelogues and evaluation over 20 locations show promising results on both travelogue mining and location overview generation.
REFERENCES
Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.
| |
1
|
Flickr. http://www.flickr.com/
|
| |
2
|
L. Kennedy and M. Naaman. Generating diverse and representative image search results for landmarks. WWW, 2008.
|
| |
3
|
I. Simon, N. Snavely, and S. M. Seitz. Scene summarization for online image collections. ICCV, 2007.
|
| |
4
|
F. Jing, L. Zhang, and W.-Y. Ma. VirtualTour: an online travel assistant based on high quality images. MM, 2006.
|
| |
5
|
L. Kennedy et al. How Flickr helps us make sense of the world: context and content in community-contributed media collections. MM, 2007.
|
| |
6
|
E. Moxley, J. Kleban, and B. S. Manjunath. SpiritTagger: a geo-aware tag suggestion tool mined from Flickr. MIR, 2008.
|
| |
7
|
T. Hofmann. Probabilistic latent semantic analysis. UAI, 1999.
|
| |
8
|
Q. Mei et al. A probabilistic approach to spatiotemporal theme pattern mining on weblogs. WWW, 2006.
|
| |
9
|
Q. Mei et al. Topic modeling with network regularization. WWW, 2008.
|
| |
10
|
R. M. Neal and G. E. Hinton. A view of the EM algorithm that justifies incremental, sparse, and other variants. Learning in Graphical Models, MIT Press, 1999.
|
| |
11
|
R. Cilibrasi and P. Vitányi. The Google similarity distance. IEEE Trans. Knowl. Data Eng, 19(3):370--383, 2007.
|
| |
12
|
IgoUgo. http://www.igougo.com/
|
|