立体视觉下稀疏编码图像统计特性的开发
Exploitation of Image Statistics with Sparse Coding in the Case of Stereo Vision
摘要：
稀疏编码算法已被用作哺乳动物视觉早期处理的模型。它已被假设，大脑使用稀疏编码，以利用感官流的统计特性。我们假设稀疏编码从数据集中发现模式，其可用于通过简单读出来估计一组刺激参数。在这项研究中，我们选择了一个立体视觉模型来验证我们的假设。我们使用局部竞争算法（LCA），然后使用朴素贝叶斯分类器来推断立体视差。从结果中，我们报告了三个观察结果。首先，视差推理是成功的，这种自然主义的处理管道。其次，需要一个扩展的，高度冗余的表示来鲁棒地识别输入模式。第三，可以从LCA表示中的有效系数的数量预测推断误差。我们的结论是，稀疏编码可以产生一个合适的一般表示为后续的推理任务。关键词：稀疏编码;局部竞争算法;高效编码;紧凑编码;概率推理;立体视觉
英文摘要：
The sparse coding algorithm has served as a model for early processing in mammalian vision. It has been assumed that the brain uses sparse coding to exploit statistical properties of the sensory stream. We hypothesize that sparse coding discovers patterns from the data set, which can be used to estimate a set of stimulus parameters by simple readout. In this study, we chose a model of stereo vision to test our hypothesis. We used the Locally Competitive Algorithm (LCA), followed by a naïve Bayes classifier, to infer stereo disparity. From the results we report three observations. First, disparity inference was successful with this naturalistic processing pipeline. Second, an expanded, highly redundant representation is required to robustly identify the input patterns. Third, the inference error can be predicted from the number of active coefficients in the LCA representation. We conclude that sparse coding can generate a suitable general representation for subsequent inference tasks. Keywords: Sparse coding; Locally Competitive Algorithm (LCA); Efficient coding; Compact code; Probabilistic inference; Stereo vision
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