Improve ranking by using image information

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4425 LNCS, Issue , 2007, Pages 645-652

  Yu, Qing ^a   Shi, Shuming ^a   Li, Zhiwei ^a   Wen, Ji Rong ^a   Ma, Wei Ying ^a  

^a MICROSOFT RESEARCH ASIA   (China)

Author keywords

Image importance; Image information; Relevance; Web search

Indexed keywords

COMPUTATION THEORY; QUERY PROCESSING; SEARCH ENGINES; TEXT PROCESSING; WEBSITES;

IMAGE IMPORTANCE; IMAGE INFORMATION; RELEVANCE;

IMAGE PROCESSING;

EID: 37149028218     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-71496-5_62     Document Type: Conference Paper

References (12)

1. Brin, S., and Page, L. The Anatomy of a Large-Scale Hypertextual Web Search Engine. WWW-7, 1998
2. Cai, D., He, X., Li, Z., Ma, W.Y., and Wen, J.R. Hierarchical Clustering of WWW Image Search Results Using Visual, Textual and Link Information. In Proc. Of the 12th ACM international conference on Multimedia, 2004.
3. Frankel, C., Swain, M., and Athitsos, V. Webseer: an Image Search Engine for the World Wide Web. University of Chicago Technical Report TR96-14, 1996.
4. Hu, J., and Bagga, A. Categorizing Images in Web Documents. SPIE Document Recognition and Retrieval (DR&R X), Santa Clara, January 2003.
5. Ivory, M.Y., and Hearst, M.A. Improving Web Site Design. IEEE Internet Computing, v.6 n.2, p.56-63, March 2002
6. Jarvelin, K. and Kekalainen, J. IR evaluation methods for retrieving highly relevant documents. In SIGIR 2000.
7. Joachims, T. Optimizing Search Engines Using Clickthrough Data. In: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining.
8. Lempel, R., and Soffer, A. PicASHOW: Pictorial Authority Search by Hyperlinks on the Web. In Proc. 10th Int. World Wide Web Conf., pp. 438-448, Hong Kong, China, 2001.
9. Robertson, S.E., Walker, S., and Beaulieu, M. Okapi at TREC-7: Automatic Ad-hoc, Filtering, VLC and Filtering Tracks. In Proceedings of TREC'99.
10. Shi, S., Song, R., and Wen, J.R. Latent Additivity: Combining Homogeneous Evidence. Technique report, MSR-TR-2006-110, Microsoft Research, August 2006.
11. Smith, J.R. and Chang, S. VisualSeek: A Fully Automated Content-Based Image Query System. In Proc. ACM Conf. Multimedia, ACM Press, New York, 1996.
12. Song, R., Liu, H., Wen, J.R., and Ma, W.Y. Learning Block Importance Models for Web Pages. WWW'2004.