Along with the rapid development of computer and image processing technology, it is definitely convenient to obtain various images for subjects, which can be more robust to classification as more feature information is contained. However, how to effectively exploit the rich discriminative information within image sets is the key problem. In this paper, based on the concept of dual linear regression classification method for image set classification, we propose a novel discriminative framework to exploit the superiority of discriminant regression mechanism. We aim to learn a projection matrix to force the represented image points from the same class to be close and those from different class are better separated. The feature extraction strategy in our discriminative framework can appropriately work with the corresponding classification strategy, thus, better classification performance can be achieved. Moreover, we propose a kernel discriminative extension method to address the non-linearity problem by adopting the kernel trick. From the experimental results, our proposed method can obtain competitive recognition rates on face recognition tasks via mapping the original image sets into a more discriminative feature space. Besides, it also shows the effectiveness for object classification task with small image sizes and different number of frames.

中文翻译：

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面向图像集的双重线性判别回归分类及其核扩展

随着计算机和图像处理技术的飞速发展，为对象获取各种图像绝对是方便的，因为包含了更多的特征信息，对分类的鲁棒性更高。但是，如何有效利用图像集中丰富的判别信息是关键问题。本文基于双重线性回归分类方法对图像集进行分类的概念，提出了一种新的判别框架，以利用判别回归机制的优越性。我们旨在学习一个投影矩阵，以迫使来自同一类别的代表图像点彼此接近，而来自不同类别的代表图像点则更好地分离。我们的区分框架中的特征提取策略可以与相应的分类策略配合使用，因此，可以实现更好的分类性能。此外，我们提出了一种核判别性扩展方法，通过采用核技巧来解决非线性问题。从实验结果来看，我们提出的方法可以通过将原始图像集映射到更具区分性的特征空间中来获得人脸识别任务的竞争识别率。此外，它还显示了在小图像尺寸和不同帧数的情况下进行对象分类任务的有效性。通过将原始图像集映射到更具区分性的特征空间中，我们提出的方法可以在人脸识别任务上获得竞争性识别率。此外，它还显示了在小图像尺寸和不同帧数的情况下进行对象分类任务的有效性。通过将原始图像集映射到更具区分性的特征空间中，我们提出的方法可以在人脸识别任务上获得具有竞争力的识别率。此外，它还显示了在小图像尺寸和不同帧数的情况下进行对象分类任务的有效性。