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Multiple kernel dimensionality reduction based on collaborative representation for set oriented image classification

机译：基于协同表示的多核降维用于面向集合的图像分类

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摘要

Given that collaborative representation (CR) methods have achieved great success in traditional single image based classification, recently, researchers have exploited the mechanism of collaborative representation to handle the case of image set based classification problem. However, without considering a proper criterion for feature extraction, this extension of collaborative representation mechanism suffers from the misleading coefficients of the incorrect classes on the uncontrolled datasets with small class separability. To address this limitation, inspired by large margin principle in discriminative analysis that aims to separately exploit the inter-class and intra-class variability, this paper proposes a novel theoretical framework of set oriented multiple kernel learning for dimensionality reduction based on collaborative representation classification. To achieve this framework, we integrate the learning of an optimal kernel from the multiple base kernels and a discriminative projection into a unified formulation. Moreover, robust feature information can be effectively extracted by minimizing the intra-class reconstruction residual and maximizing the inter-class reconstruction residual of the regularized hull modeled for the image sets. Since the criterion of feature extraction conforms to the mechanism of the collaborative representation classifier, the collaborative representation coefficients in our model can be much discriminative across classes. Notably, this research has important theoretical significance in improving the classification performance for collaborative representation classifier from the perspective of large margin discriminative learning. By employing the method of trace ratio maximization, we also develop a framework to solve the resulting nonconvex optimization problem efficiently. Extensive experiments on benchmark datasets well demonstrate the effectiveness of the proposed method. (C) 2019 Elsevier Ltd. All rights reserved.
机译：鉴于协作表示（CR）方法在传统的基于单个图像的分类中已经取得了巨大的成功，最近，研究人员已经利用协作表示的机制来处理基于图像集的分类问题。然而，在不考虑特征提取的适当标准的情况下，协作表示机制的这种扩展遭受了具有不可分离的小类的非受控数据集上不正确类的误导系数的困扰。为了解决这一局限性，受判别分析中的大余量原理的启发，该判别分析旨在分别利用类间和类内变异性，本文提出了一种基于集的多核学习的新理论框架，该模型可基于协作表示分类进行降维。为了实现此框架，我们将对来自多个基本内核的最佳内核的学习和一个判别式投影集成到一个统一的公式中。此外，通过最小化为图像集建模的正则化船体的类内重构残差和最大化类间重构残差，可以有效地提取鲁棒的特征信息。由于特征提取的标准符合协同表示分类器的机制，因此我们模型中的协同表示系数在各个类之间可能具有很大的区别性。值得注意的是，从大幅度判别学习的角度来看，这项研究对于提高协作表示分类器的分类性能具有重要的理论意义。通过采用迹线比率最大化的方法，我们还开发了一个框架来有效解决由此产生的非凸优化问题。在基准数据集上进行的大量实验很好地证明了该方法的有效性。 （C）2019 Elsevier Ltd.保留所有权利。

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