Image set classification has recently received much attention due to its various applications in pattern recognition and computer vision. To compare and match image sets, the major challenges are to devise an effective and efficient representation and to define a measure of similarity between image sets. In this paper, we propose a method for representing image sets based on block-diagonal Covariance Descriptors (CovDs). In particular, the proposed image set representation is in the form of non-singular covariance matrices, also known as Symmetric Positive Definite (SPD) matrices, that lie on Riemannian manifold. By dividing each image of an image set into square blocks of the same size, we compute the corresponding block CovDs instead of the global one. Taking the relative discriminative power of these block CovDs into account, a block-diagonal SPD matrix can be constructed to achieve a better discriminative capability. We extend the proposed approach to work with bidirectional CovDs and achieve a further boost in performance. The resulting block-diagonal SPD matrices combined with Riemannian metrics are shown to provide a powerful basis for image set classification. We perform an extensive evaluation on four datasets for several image set classification tasks. The experimental results demonstrate the effectiveness and efficiency of the proposed method.

由于图像集分类在模式识别和计算机视觉中的各种应用，近来受到了广泛关注。为了比较和匹配图像集，主要的挑战是设计一种有效的表示方法，并定义图像集之间的相似性度量。在本文中，我们提出了一种基于块对角协方差描述符（CovDs）的图像集表示方法。特别地，所提出的图像集表示形式为位于黎曼流形上的非奇异协方差矩阵，也称为对称正定（SPD）矩阵。通过将图像集中的每个图像划分为相同大小的正方形块，我们可以计算对应的块CovD，而不是全局块。考虑到这些区块CovD的相对区分能力，可以构建块对角SPD矩阵以实现更好的判别能力。我们将提出的方法扩展为可与双向CovD配合使用，并进一步提高性能。结果表明，将得到的块对角SPD矩阵与黎曼度量结合起来，可以为图像集分类提供强大的基础。我们对四个图像集分类任务的四个数据集进行了广泛的评估。实验结果证明了该方法的有效性和有效性。结果表明，将得到的块对角SPD矩阵与黎曼度量结合起来，可以为图像集分类提供强大的基础。我们对四个图像集分类任务的四个数据集进行了广泛的评估。实验结果证明了该方法的有效性和有效性。结果表明，将得到的块对角SPD矩阵与黎曼度量结合起来，可以为图像集分类提供强大的基础。我们对四个图像集分类任务的四个数据集进行了广泛的评估。实验结果证明了该方法的有效性和有效性。