Dual Diversified Dynamical Gaussian Process Latent Variable Model for Video Repairing

Abstract

In this paper, we propose a dual diversified dynamical Gaussian process latent variable model ( [Formula: see text]GPLVM) to tackle the video repairing issue. For preservation purposes, videos have to be conserved on media. However, storing on media, such as films and hard disks, can suffer from unexpected data loss, for instance, physical damage. So repairing of missing or damaged pixels is essential for better video maintenance. Most methods seek to fill in missing holes by synthesizing similar textures from local patches (the neighboring pixels), consecutive frames, or the whole video. However, these can introduce incorrect contexts, especially when the missing hole or number of damaged frames is large. Furthermore, simple texture synthesis can introduce artifacts in undamaged and recovered areas. To address aforementioned problems, we introduce two diversity encouraging priors to both of inducing points and latent variables for considering the variety in existing videos. In [Formula: see text]GPLVM, the inducing points constitute a smaller subset of observed data, while latent variables are a low-dimensional representation of observed data. Since they have a strong correlation with the observed data, it is essential that both of them can capture distinct aspects of and fully represent the observed data. The dual diversity encouraging priors ensure that the trained inducing points and latent variables are more diverse and resistant for context-aware and artifacts-free-based video repairing. The defined objective function in our proposed model is initially not analytically tractable and must be solved by variational inference. Finally, experimental testing results illustrate the robustness and effectiveness of our method for damaged video repairing.

Improved sparse representation method for image classification

“…Based on the above analyses, h 1 (Z) is redefined as (see (4) and 5) Thus, the partial derivative over Z k of h 1 (Z) is (see (5) and (6)) then (see (6)) Let…”

“…Image classification [1,2], as a computer vision technology [3][4][5][6][7], has been developing rapidly. Meanwhile, it has also attracted considerable attention in recent years [8].…”

Informed Non-convex Robust Principal Component Analysis with Features

“…The anomalous asymmetry in the two sign models is no longer observed in non-convex algorithms. Next, we highlight the speed of our algorithm for large-scale matrices, typical of video sequences Xiong et al [2016]. 1500×1500 to 2500×2500 random observation matrices are generated, where the rank is chosen to be 20% of the column number and random sign error corrupts 11% of the entries, with features X, Y having a dimension of 50% of the column number.…”

A Variant of Gaussian Process Dynamical Systems

“…by periodic kernels. VGPDSs were also applied in many fields, such as phoneme classification [19], video repairing [20] and multi-task motion modeling [21]. The VDM-GPDS considers the dependence of multiple outputs and introduces convolution processes to explicitly depict multi-output dependence.…”