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首页> 外文会议>IEEE International Conference on Tools with Artificial Intelligence >Kernel Two Dimensional Subspace for Image Set Classification
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Kernel Two Dimensional Subspace for Image Set Classification

机译：图像集分类的核二维子空间

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摘要

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.
机译：由于在互联网，监视系统，社交媒体网络和自动驾驶汽车上可获得大量数据，因此大型视频上的对象识别最近引起了相当大的研究兴趣。通过将大型视频表示为图像集，我们可以处理复杂的数据变化，例如视点，照明和姿势。在本文中，我们提出了一种基于核正交互子空间方法（KOMSM）的有效而鲁棒的图像集识别方法，其中图像集被表示为非线性子空间。在我们的方法中，我们通过应用K2D-PCA和2D-PCA的变体将图像集公式化为非线性2D子空间。与KOMSM相比，该方法继承了2D-PCA及其变体的计算优势，因此需要较少的内存资源。此外，由K2D-PCA生成的子空间保留了图像像素之间的空间关系，比KOMSM生成更多的信息子空间。引入的方法的优点在于，可以以更紧凑的方式表示子空间，实现较低的时间复杂度，从而确认使用2D-PCA及其变体的适用性。通过对五个公开可用的数据集进行的全面实验，揭示了这些结果。

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