Image structure subspace learning using structural similarity index

Abstract

Literature has shown that Mean Squared Error is not a promising measure for image fidelity and similarity assessment, and Structural Similarity Index (SSIM) can properly handle this aspect. The existing subspace learning methods in machine learning are based on Euclidean distance or MSE and thus cannot properly capture the structural features of images. In this paper, we define image structure subspace which captures the intrinsic structural features of an image and discriminates the different types of image distortions. Therefore, this paper provides a bridge between image processing and manifold learning opening future research opportunities. In order to learn this subspace, we propose SSIM kernel as a new kernel which can be used in kernel-based machine learning methods.