TY - GEN
T1 - Bipartite graph reinforcement model for web image annotation
AU - Rui, Xiaoguang
AU - Li, Mingjing
AU - Li, Zhiwei
AU - Ma, Wei-Ying
AU - Yu, Nenghai
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2007
Y1 - 2007
N2 - Automatic image annotation is an effective way for managing and retrieving abundant images on the internet. In this paper, a bipartite graph reinforcement model (BGRM) is proposed for web image annotation. Given a web image, a set of candidate annotations is extracted from its surrounding text and other textual information in the hosting web page. As this set is often incomplete, it is extended to include more potentially relevant annotations by searching and mining a large-scale image database. All candidates are modeled as a bipartite graph. Then a reinforcement algorithm is performed on the bipartite graph to re-rank the candidates. Only those with the highest ranking scores are reserved as the final annotations. Experimental results on real web images demonstrate the effectiveness of the proposed model. Copyright 2007 ACM.
AB - Automatic image annotation is an effective way for managing and retrieving abundant images on the internet. In this paper, a bipartite graph reinforcement model (BGRM) is proposed for web image annotation. Given a web image, a set of candidate annotations is extracted from its surrounding text and other textual information in the hosting web page. As this set is often incomplete, it is extended to include more potentially relevant annotations by searching and mining a large-scale image database. All candidates are modeled as a bipartite graph. Then a reinforcement algorithm is performed on the bipartite graph to re-rank the candidates. Only those with the highest ranking scores are reserved as the final annotations. Experimental results on real web images demonstrate the effectiveness of the proposed model. Copyright 2007 ACM.
KW - Automatic image annotation
KW - Bipartite graph model
UR - http://www.scopus.com/inward/record.url?scp=38049059799&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-38049059799&origin=recordpage
U2 - 10.1145/1291233.1291378
DO - 10.1145/1291233.1291378
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9781595937025
T3 - Proceedings of the ACM International Multimedia Conference and Exhibition
SP - 585
EP - 594
BT - Proceedings of the Fifteenth ACM International Conference on Multimedia, MM'07
T2 - 15th ACM International Conference on Multimedia, MM'07
Y2 - 24 September 2007 through 29 September 2007
ER -