TY - GEN
T1 - A mutual semantic endorsement approach to image retrieval and context provision
AU - Li, Jia
PY - 2005/11/10
Y1 - 2005/11/10
N2 - Learning semantics from annotated images to enhance content-based retrieval is an important research direction. In this paper, annotation data are assumed available for only a subset of images inside the database. An on the fly learning method is developed to capture the semantics of query images. Specifically, the semantics of annotated images in a visual proximity of a query are compared with each other to determine the amount of mutual endorsement. An image is considered endorsed by another if they possess similar semantics. Annotations with high mutual endorsement are used to narrow down a candidate pool of images. The new retrieval method is inherently dynamic and treats seamlessly different forms of annotation data. Experiments show that semantic endorsement can increase precision by as much as 70% in average for a wide range of parameter settings. We also develop a context provision mechanism to reveal the relationship between a query and semantic clusters extracted from the database. Context helps users explore the content of a database and provides a platform for them to tailor searches by stressing different perspectives in the interpretation of a query.
AB - Learning semantics from annotated images to enhance content-based retrieval is an important research direction. In this paper, annotation data are assumed available for only a subset of images inside the database. An on the fly learning method is developed to capture the semantics of query images. Specifically, the semantics of annotated images in a visual proximity of a query are compared with each other to determine the amount of mutual endorsement. An image is considered endorsed by another if they possess similar semantics. Annotations with high mutual endorsement are used to narrow down a candidate pool of images. The new retrieval method is inherently dynamic and treats seamlessly different forms of annotation data. Experiments show that semantic endorsement can increase precision by as much as 70% in average for a wide range of parameter settings. We also develop a context provision mechanism to reveal the relationship between a query and semantic clusters extracted from the database. Context helps users explore the content of a database and provides a platform for them to tailor searches by stressing different perspectives in the interpretation of a query.
UR - http://www.scopus.com/inward/record.url?scp=34548225835&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=34548225835&partnerID=8YFLogxK
U2 - 10.1145/1101826.1101856
DO - 10.1145/1101826.1101856
M3 - Conference contribution
AN - SCOPUS:34548225835
T3 - MIR 2005 - Proceedings of the 7th ACM SIGMM International Workshop on Multimedia Information Retrieval, Co-located with ACM Multimedia 2005
SP - 173
EP - 182
BT - MIR 2005 - Proceedings of the 7th ACM SIGMM International Workshop on Multimedia Information Retrieval, Co-located with ACM Multimedia 2005
PB - Association for Computing Machinery, Inc
T2 - 7th ACM SIGMM International Workshop on Multimedia Information Retrieval, MIR 2005
Y2 - 10 November 2005 through 11 November 2005
ER -