面对网络图像的爆炸性增长,网络图像标注成为近年来一个热点研究内容,稀疏特征选择在提升网络图像标注效率和性能方面发挥着重要的作用.提出了一种增强稀疏性特征选择算法,即,基于l_2,1/2矩阵范数和共享子空间的半监督稀疏特征选择算法(semi-supervised sparse feature selection based on l_2,1/2-matix norm with shared subspace learning,简称SFSLS)进行网络图像标注.在SFSLS算法中,应用l_2,1/2矩阵范数来选取最稀疏和最具判别性的特征,通过共享子空间学习,考虑不同特征之间的关联信息.另外,基于图拉普拉斯的半监督学习,使SFSLS算法同时利用了有标签数据和无标签数据.设计了一种有效的迭代算法来最优化目标函数.SFSLS算法与其他稀疏特征选择算法在两个大规模网络图像数据库上进行了比较,结果表明,SFSLS算法更适合于大规模网络图像的标注.

In dealing with the explosive growth of web images, Web image annotation has become a critical research issue in recent years. Sparse feature selection plays an important role in improving the efficiency and performance of Web image annotation. In this paper, a feature selection framework is proposed with enhanced sparsity for Web image annotation. The new framework, termed as semi-supervised sparse feature selection based on l_2,1/2-matix norm with shared subspace learning (SFSLS), selects the most sparse and discriminative features by utilizing l_2,1/2-matix norm and obtains the correlation between different features via shared subspace learning. In addition, SFSLS uses graph Laplacian semi-supervised learning to exploit both labeled and unlabeled data simultaneously. An efficient iterative algorithm is designed to optimize the objective function. SFSLS method is compared to other feature selection algorithms on two Web image datasets and the results indicate it is suitable for large-scale Web image annotation.

国家重点基础研究发展计划(973)(2012CB316304); 国家自然科学基金(61471032, 61472030); 河北省高等学校科学技术研究青年基金(QN2014026); 教育部创新团队项目(IRT201206); 北京市现代信息科学与网络技术重点实验室资助项目