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Alternative TitleThe Research of Image Annotation based on Hypergraph Transduction Non-negative Matrix Factorization
李冰锋; 唐延东; 韩志
Source Publication计算机仿真
Volume34Issue:2Pages:380-384, 440
Contribution Rank1
Keyword图像标注 流形学习 超图直推 非负矩阵分解
Other AbstractThis article proposes an image annotation algorithm based on non-negative matrix factorization of hypergraph transduction. Firstly, the regularization principle of supervising hypergraph was introduced into frame of non-negative matrix factorization. The image annotation utilized complex multi-relationship among samples and annotation information effectively. Meanwhile, rational control ability of algorithm for forecast error of label was increased to use regular terms of transduction learning. Results of simulation on data set of image annotation show that the algorithm improves accuracy of annotation and robustness of model significantly compared with traditional image annotation algorithms such as support vector machine and measurement learning with discrimination type. It has excellent feasibility and validity
Document Type期刊论文
Corresponding Author李冰锋
Recommended Citation
GB/T 7714
李冰锋,唐延东,韩志. 基于超图直推非负矩阵分解的图像标注法研究[J]. 计算机仿真,2017,34(2):380-384, 440.
APA 李冰锋,唐延东,&韩志.(2017).基于超图直推非负矩阵分解的图像标注法研究.计算机仿真,34(2),380-384, 440.
MLA 李冰锋,et al."基于超图直推非负矩阵分解的图像标注法研究".计算机仿真 34.2(2017):380-384, 440.
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