Kernel Two Dimensional Subspace for Image Set Classification

Object recognition on large-scale video has recently attracted considerable research interest due to the huge amount of data available on the Internet, surveillance systems, social media networks and autonomous vehicles. By representing large-scale videos as image sets, we can handle the complex data variations such as viewpoint, illumination, and pose. In this paper, we propose an efficient and robust method for image set recognition based on Kernel Orthogonal Mutual Subspace Method (KOMSM), where sets of images are expressed as nonlinear subspaces. In our method, we formulate the image sets as nonlinear 2D subspaces by applying K2D-PCA and variants of 2D-PCA. Comparing to KOMSM, the proposed method requires less memory resource since it inherits the computational advantages of 2D-PCA and variants. In addition, the subspaces produced by K2D-PCA preserves the spatial relation between image pixels, generating more informative subspaces than KOMSM. The introduced method has the advantage of representing the subspaces in a more compact manner, achieving lower time complexity, confirming the suitability of employing 2D-PCA and variants. These results have been revealed through comprehensive experimentation conducted on five publicly available datasets.