新型的图像检索最优实验设计算法

摘要

Most of the existing optimal experimental design (OED) methods are based on either linear regression model or Laplacian regularized least square (LapRLS) model. This paper proposes a new active learning algorithm based on the second-order Hessian energy, which has the manifold learning capability. The algorithm selects those optimal samples which minimize the parameter covariance matrix of the Hessian regularized regression model, and overcomes the drawbacks of LapRLS. The experimental results on content-based image retrieval have demonstrated the effectiveness of the proposed approach.%大部分现有的最优实验设计方法是基于线性回归或拉普拉斯正则最小二乘模型(LapRLS)的.提出一种基于二阶Hessian能并具有流形学习能力的主动学习算法,该算法选择那些能使Hessiar正则回归模型的参数协方差矩阵最小化的样本作为最优样本,可以克服LapRLS的依赖特定常量及缺乏推算能力等缺点.基于内容的图像检索实验证明了该方法的有效性.