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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Sparse Coding From a Bayesian Perspective
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Sparse Coding From a Bayesian Perspective

机译：贝叶斯视角的稀疏编码

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摘要

Sparse coding is a promising theme in computer vision. Most of the existing sparse coding methods are based on either $ell_{0}$ or $ell_{1}$ penalty, which often leads to unstable solution or biased estimation. This is because of the nonconvexity and discontinuity of the $ell_{0}$ penalty and the over-penalization on the true large coefficients of the $ell_{1}$ penalty. In this paper, sparse coding is interpreted from a novel Bayesian perspective, which results in a new objective function through maximum a posteriori estimation. The obtained solution of the objective function can generate more stable results than the $ell_{0}$ penalty and smaller reconstruction errors than the $ell_{1}$ penalty. In addition, the convergence property of the proposed algorithm for sparse coding is also established. The experiments on applications in single image super-resolution and visual tracking demonstrate that the proposed method is more effective than other state-of-the-art methods.
机译：稀疏编码是计算机视觉中有希望的主题。现有的大多数稀疏编码方法都基于$ ell_ {0} $或$ ell_ {1} $损失，这通常会导致求解不稳定或估计有偏差。这是因为$ ell_ {0} $罚分的非凸性和不连续性以及$ ell_ {1} $罚分的真大系数的过度惩罚。在本文中，从新颖的贝叶斯角度解释了稀疏编码，这通过最大的后验估计产生了新的目标函数。获得的目标函数解比$ ell_ {0} $罚分可以产生更稳定的结果，并且比$ ell_ {1} $罚分可以产生更小的重构误差。另外，还建立了所提出的稀疏编码算法的收敛性。在单图像超分辨率和视觉跟踪中的应用实验表明，该方法比其他最新方法更有效。

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