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Automatic image annotation using semi-supervised multi-instance multi-label learning algorithm
Songhe Feng; De Xu; Bing Li
2008
发表期刊Chinese Journal of Electronics
卷号17期号:4页码:602-606
摘要Automatic image annotation has emerged as an important research topic. From the perspective of machine learning, the annotation task fits both multi-instance and multi-label learning framework due to the fact that an image is composed of multiple regions, and is as-sociated with multiple keywords as well. In this paper, we propose a novel Semi-supervised multi-instance multi-label (SSMIML) learning framework, which aims at taking full advantage of both labeled and unlabeled data to address the annotation problem. Specifically, a reinforced diverse density algorithm is applied firstly to select the Instance prototypes (IPs) with respect to a given keyword from both positive and unlabeled bags. Then, the selected IPs are modeled using the Gaussian mixture model (GMM) in order to reflect the semantic class density distribution. Furthermore, based on the class distribution for a keyword, both positive and unlabeled bags are redefined using a novel feature mapping strategy. Thus, each bag can be represented by one fixed-length feature vector so that the manifold-ranking algorithm can be used subsequently to propagate the corresponding label from positive bags to unlabeled bags directly. Experiments on the Corel data set show that the proposed method outperforms most existing image annotation algorithms.
关键词Image Annotation
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/20393
专题09年以前成果
作者单位Beijing Jiaotong University
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GB/T 7714
Songhe Feng,De Xu,Bing Li. Automatic image annotation using semi-supervised multi-instance multi-label learning algorithm[J]. Chinese Journal of Electronics,2008,17(4):602-606.
APA Songhe Feng,De Xu,&Bing Li.(2008).Automatic image annotation using semi-supervised multi-instance multi-label learning algorithm.Chinese Journal of Electronics,17(4),602-606.
MLA Songhe Feng,et al."Automatic image annotation using semi-supervised multi-instance multi-label learning algorithm".Chinese Journal of Electronics 17.4(2008):602-606.
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