In dictionary learning for analysis of images, spatial correlation from extracted patches can be lever-aged to improve characterization power. We propose a Bayesian framework for dictionary learning, with spatial location dependencies captured by imposing a multiplicative Gaussian process (GP) priors on the latent units representing binary activations. Data augmentation and Kronecker methods allow for efficient Markov chain Monte Carlo sampling. We further extend the model with Sigmoid Belief Networks (SBNs), linking the GPs to the top-layer latent binary units of the SBN, capturing inter-dictionary dependencies while also yielding computational savings. Applications to image denoising, inpainting and depth-information restoration demonstrate that the proposed model outperforms other leading Bayesian dictionary learning approaches.
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机译：在译文学习的文章学习中，来自提取的贴片的空间相关可以是杠杆，以改善表征功率。我们提出了一个贝叶斯框架的字典学习框架，通过在代表二进制激活的潜在单元上施加乘法高斯过程（GP）前瞻来捕获空间位置依赖性。数据增强和Kronecker方法允许高效的Markov Chain Monte Carlo采样。我们进一步用SIGMOID信仰网络（SBN）扩展了模型，将GPS链接到SBN的顶层潜在二进制单元，捕获字典间差异，同时还产生计算节省。应用于图像去噪，尿素和深度信息恢复证明，所提出的模型优于其他主要的贝叶斯字典学习方法。
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