CASIA OpenIR  > 模式识别国家重点实验室  > 图像与视频分析
An adaptive graph model for automatic image annotation
Jing Liu; Mingjing Li; Wei-Ying Ma; Qingshan Liu; Hanqing Lu
2006
Conference NameACM SIGMM International Workshop on Multimedia Information Retrieval
Source PublicationProceedings of the 8th ACM SIGMM International Workshop on Multimedia Information Retrieval
Conference DateOctober 26-27, 2006
Conference PlaceSanta Barbara, California, USA
AbstractAutomatic 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.
KeywordImage Annotation Image Retrieval Manifold Ranking
Indexed ByEI
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/11065
Collection模式识别国家重点实验室_图像与视频分析
Corresponding AuthorJing Liu
Recommended Citation
GB/T 7714
Jing Liu,Mingjing Li,Wei-Ying Ma,et al. An adaptive graph model for automatic image annotation[C],2006.
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