TY - GEN
T1 - An adaptive graph model for automatic image annotation
AU - Liu, Jing
AU - Li, Mingjing
AU - Ma, Wei-Ying
AU - Liu, Qingshan
AU - Lu, Hanqing
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 - 2006
Y1 - 2006
N2 - Automatic keyword annotation is a promising solution to enable more effective image search by using keywords. In this paper, we propose a novel automatic image annotation method based on manifold ranking learning, in which the visual and textual information are well integrated. Due to complex and unbalanced data distribution and limited prior information in practice, we design two new schemes to make manifold ranking efficient for image annotation. Firstly, we design a new scheme named the Nearest Spanning Chain (NSC) to generate an adaptive similarity graph, which is robust across data distribution and easy to implement. Secondly, the word-to-word correlations obtained from WordNet and the pairwise co-occurrence are taken into consideration to expand the annotations and prune irrelevant annotations for each image. Experiments conducted on standard Corel dataset and web image dataset demonstrate the effectiveness and efficiency of the proposed method for image annotation. Copyright 2006 ACM.
AB - Automatic keyword annotation is a promising solution to enable more effective image search by using keywords. In this paper, we propose a novel automatic image annotation method based on manifold ranking learning, in which the visual and textual information are well integrated. Due to complex and unbalanced data distribution and limited prior information in practice, we design two new schemes to make manifold ranking efficient for image annotation. Firstly, we design a new scheme named the Nearest Spanning Chain (NSC) to generate an adaptive similarity graph, which is robust across data distribution and easy to implement. Secondly, the word-to-word correlations obtained from WordNet and the pairwise co-occurrence are taken into consideration to expand the annotations and prune irrelevant annotations for each image. Experiments conducted on standard Corel dataset and web image dataset demonstrate the effectiveness and efficiency of the proposed method for image annotation. Copyright 2006 ACM.
KW - Image annotation
KW - Image retrieval
KW - Manifold ranking
UR - https://www.scopus.com/pages/publications/34547472538
UR - https://www.scopus.com/pages/publications/34547472538#tab=impact
U2 - 10.1145/1178677.1178689
DO - 10.1145/1178677.1178689
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 1595934952
SN - 9781595934956
T3 - Proceedings of the ACM International Multimedia Conference and Exhibition
SP - 61
EP - 70
BT - Proceedings of the 8th ACM Multimedia International Workshop on Multimedia Information Retrieval, MIR 2006
T2 - 8th ACM Multimedia International Workshop on Multimedia Information Retrieval, MIR 2006, co-located with the 2006 ACM International Multimedia Conferenc
Y2 - 26 October 2006 through 27 October 2006
ER -