论文标题
学习有效的图像分类结构化词典
Learning efficient structured dictionary for image classification
论文作者
论文摘要
近年来，基于词典学习（DL）在模式分类领域中取得了成功。在本文中，我们提出了一种有效的结构化词典学习（ESDL）方法，该方法同时考虑了培训样本的多样性和标签信息。具体而言，ESDL将替代培训样本引入了字典学习过程。为了增加表示分类系数的判别能力，将理想的正则化项纳入了ESDL的目标函数中。此外，与传统的DL方法相反，该方法对系数矩阵施加了计算昂贵的L1-norm约束，ESDL采用了L2-Norm正则化项。基准数据库（包括四个面部数据库和一个场景数据集）上的实验结果表明，ESDL的表现优于先前的DL方法。更重要的是，ESDL可以在各种模式分类任务中应用。
Recent years have witnessed the success of dictionary learning (DL) based approaches in the domain of pattern classification. In this paper, we present an efficient structured dictionary learning (ESDL) method which takes both the diversity and label information of training samples into account. Specifically, ESDL introduces alternative training samples into the process of dictionary learning. To increase the discriminative capability of representation coefficients for classification, an ideal regularization term is incorporated into the objective function of ESDL. Moreover, in contrast with conventional DL approaches which impose computationally expensive L1-norm constraint on the coefficient matrix, ESDL employs L2-norm regularization term. Experimental results on benchmark databases (including four face databases and one scene dataset) demonstrate that ESDL outperforms previous DL approaches. More importantly, ESDL can be applied in a wide range of pattern classification tasks.
