Natural image coding in V1: how much use is orientation selectivity?
Natural image coding in V1: how much use is orientation selectivity?
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DOI：
10.1371/journal.pcbi.1000336
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发表时间：
2009-04
影响因子：
4.3
通讯作者：
Bethge M
中科院分区：
文献类型：
--
作者：
Eichhorn J;Sinz F;Bethge M
方向选择性是V1中简单细胞编码的最显著特征，已被证明是在各种统计图像模型中自然图像中高阶相关性的减少中出现的。在这些模型中最简约的是线性独立分量分析（ICA），而二阶去相关变换，如主分量分析（PCA）不产生定向滤波器。由于这一发现，有人建议，方向选择性的出现可以解释高阶冗余减少。为了评估这一假设的可行性，这是一个重要的经验问题，与PCA或其他二阶去相关方法相比，ICA可以去除多少冗余。虽然以前的一些研究已经得出结论，自然图像中的高阶相关性通常是微不足道的，但其他研究报告ICA的额外增益超过100%。只有通过发展可靠的定量评价方法，才能得出关于高阶相关性在自然图像中的作用的一致结论。在这里，我们提出了一个非常仔细和全面的分析，使用三个评估标准相关的冗余减少：除了多信息和平均对数损失，我们计算完整的率失真曲线ICA相比，PCA。无一例外，我们发现ICA滤波器的优势很小。同时，我们表明，一个简单的球对称分布，只有两个参数可以拟合数据显着优于概率模型的ICA。这一发现表明，虽然在自然图像中的高阶相关性的量实际上是显着的，方向选择性的功能并没有产生很大的贡献，冗余度减少内的线性滤波器组模型的V1简单的细胞。自从Hubel和Wittelman获得诺贝尔奖以来，人们已经知道方向选择性是初级视觉皮层中简单细胞的一个重要特征。这个视觉处理阶段的标准描述是线性滤波器组，其中每个神经元响应视野内某个位置处的定向边缘。从视觉科学家的角度来看，我们想了解为什么方向选择滤波器组提供了有用的图像表示。先前的几项研究表明，方向选择性出现时，根据自然图像的统计数据优化的个别过滤器的形状。在这里，我们定量研究方向选择性的功能是多么关键，这种优化。我们发现，有一个大范围的非定向过滤器的形状，以及执行的最佳方向选择性过滤器。我们的结论是，标准的滤波器组模型是不适合揭示方向选择性和自然图像的统计之间的强联系。因此，为了理解初级视觉皮层中方向选择性的作用，我们必须开发更复杂的自然图像的非线性模型。
Orientation selectivity is the most striking feature of simple cell coding in V1 that has been shown to emerge from the reduction of higher-order correlations in natural images in a large variety of statistical image models. The most parsimonious one among these models is linear Independent Component Analysis (ICA), whereas second-order decorrelation transformations such as Principal Component Analysis (PCA) do not yield oriented filters. Because of this finding, it has been suggested that the emergence of orientation selectivity may be explained by higher-order redundancy reduction. To assess the tenability of this hypothesis, it is an important empirical question how much more redundancy can be removed with ICA in comparison to PCA or other second-order decorrelation methods. Although some previous studies have concluded that the amount of higher-order correlation in natural images is generally insignificant, other studies reported an extra gain for ICA of more than 100%. A consistent conclusion about the role of higher-order correlations in natural images can be reached only by the development of reliable quantitative evaluation methods. Here, we present a very careful and comprehensive analysis using three evaluation criteria related to redundancy reduction: In addition to the multi-information and the average log-loss, we compute complete rate–distortion curves for ICA in comparison with PCA. Without exception, we find that the advantage of the ICA filters is small. At the same time, we show that a simple spherically symmetric distribution with only two parameters can fit the data significantly better than the probabilistic model underlying ICA. This finding suggests that, although the amount of higher-order correlation in natural images can in fact be significant, the feature of orientation selectivity does not yield a large contribution to redundancy reduction within the linear filter bank models of V1 simple cells. Since the Nobel Prize winning work of Hubel and Wiesel it has been known that orientation selectivity is an important feature of simple cells in the primary visual cortex. The standard description of this stage of visual processing is that of a linear filter bank where each neuron responds to an oriented edge at a certain location within the visual field. From a vision scientist's point of view, we would like to understand why an orientation selective filter bank provides a useful image representation. Several previous studies have shown that orientation selectivity arises when the individual filter shapes are optimized according to the statistics of natural images. Here, we investigate quantitatively how critical the feature of orientation selectivity is for this optimization. We find that there is a large range of non-oriented filter shapes that perform nearly as well as the optimal orientation selective filters. We conclude that the standard filter bank model is not suitable to reveal a strong link between orientation selectivity and the statistics of natural images. Thus, to understand the role of orientation selectivity in the primary visual cortex, we will have to develop more sophisticated, nonlinear models of natural images.
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影响因子：
5.4
作者：
ATTNEAVE, F
通讯作者：
ATTNEAVE, F
影响因子：
1.8
作者：
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DOI：
10.1364/josaa.24.000922
发表时间：
2007-04-01
影响因子：
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DOI：
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发表时间：
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影响因子：
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作者：
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影响因子：
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作者：
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