CASIA OpenIR  > 09年以前成果
Automatic region-based image annotation using an improved multiple-instance learning algorithm
Songhe Feng; De Xu; Bing Li
Source PublicationChinese Journal of Electronics
2008
Volume17Issue:1Pages:43-47
AbstractMany existing image annotation algorithms work under probabilistic modeling mechanism. In this paper, we formulate the problem as a variation of supervised learning task and propose an Improved CitationkNN (ICKNN) Multiple-instance learning (MIL) algorithm for automatic image annotation. In contrast with the existing MIL based image annotation algorithm which intends to learn an explicit correspondence between image regions and keywords, here we annotate the keywords on the entire image instead of its regions. Concretely, we first explore the concept of Confidence weight (CW) for every training bag (image) to reflect the relevance extent between a bag and a semantic keyword. It can be treated as a stage of re-ranking on training set before annotation starts. Moreover, a modified hausdorff distance is adopted for the ICKNN algorithm to solve the automatic annotation problem. The proposed annotation approach demonstrates a promising performance over 5,000 images from COREL dataset, as compared with some current algorithms in the literature.
KeywordImage Annotation
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/20391
Collection09年以前成果
AffiliationBeijing Jiaotong University
Recommended Citation
GB/T 7714
Songhe Feng,De Xu,Bing Li. Automatic region-based image annotation using an improved multiple-instance learning algorithm[J]. Chinese Journal of Electronics,2008,17(1):43-47.
APA Songhe Feng,De Xu,&Bing Li.(2008).Automatic region-based image annotation using an improved multiple-instance learning algorithm.Chinese Journal of Electronics,17(1),43-47.
MLA Songhe Feng,et al."Automatic region-based image annotation using an improved multiple-instance learning algorithm".Chinese Journal of Electronics 17.1(2008):43-47.
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