Department of Mathematics and Systems Science, National University of Defense Technology, Changsha 410073, China.
IEEE Trans Image Process. 2013 Jan;22(1):340-52. doi: 10.1109/TIP.2012.2214044. Epub 2012 Aug 17.
The problem of image classification has aroused considerable research interest in the field of image processing. Traditional methods often convert an image to a vector and then use a vector-based classifier. In this paper, a novel multiple rank regression model (MRR) for matrix data classification is proposed. Unlike traditional vector-based methods, we employ multiple-rank left projecting vectors and right projecting vectors to regress each matrix data set to its label for each category. The convergence behavior, initialization, computational complexity, and parameter determination are also analyzed. Compared with vector-based regression methods, MRR achieves higher accuracy and has lower computational complexity. Compared with traditional supervised tensor-based methods, MRR performs better for matrix data classification. Promising experimental results on face, object, and hand-written digit image classification tasks are provided to show the effectiveness of our method.
图像分类问题在图像处理领域引起了相当多的研究兴趣。传统方法通常将图像转换为向量，然后使用基于向量的分类器。本文提出了一种新的矩阵数据分类的多秩回归模型（MRR）。与传统的基于向量的方法不同，我们使用多秩左投影向量和右投影向量将每个矩阵数据集回归到其所属的类别标签。还分析了收敛行为、初始化、计算复杂度和参数确定。与基于向量的回归方法相比，MRR 具有更高的准确性和更低的计算复杂度。与传统的监督张量方法相比，MRR 更适合矩阵数据分类。在面部、物体和手写数字图像分类任务上提供了有前途的实验结果，以展示我们方法的有效性。
