Wachtler T, Lee T W, Sejnowski T J
Computational Neurobiology Laboratory, The Salk Institute, La Jolla, California 92037, USA.
J Opt Soc Am A Opt Image Sci Vis. 2001 Jan;18(1):65-77. doi: 10.1364/josaa.18.000065.
We applied independent component analysis (ICA) to hyperspectral images in order to learn an efficient representation of color in natural scenes. In the spectra of single pixels, the algorithm found basis functions that had broadband spectra and basis functions that were similar to natural reflectance spectra. When applied to small image patches, the algorithm found some basis functions that were achromatic and others with overall chromatic variation along lines in color space, indicating color opponency. The directions of opponency were not strictly orthogonal. Comparison with principal-component analysis on the basis of statistical measures such as average mutual information, kurtosis, and entropy, shows that the ICA transformation results in much sparser coefficients and gives higher coding efficiency. Our findings suggest that nonorthogonal opponent encoding of photoreceptor signals leads to higher coding efficiency and that ICA may be used to reveal the underlying statistical properties of color information in natural scenes.
我们将独立成分分析(ICA)应用于高光谱图像,以便学习自然场景中颜色的有效表示。在单个像素的光谱中,该算法发现了具有宽带光谱的基函数以及与自然反射光谱相似的基函数。当应用于小图像块时,该算法发现了一些消色差的基函数以及其他在颜色空间中沿直线具有整体色度变化的基函数,这表明了颜色对立。对立方向并非严格正交。基于平均互信息、峰度和熵等统计量与主成分分析进行比较,结果表明ICA变换产生的系数要稀疏得多,并且具有更高的编码效率。我们的研究结果表明,光感受器信号的非正交对立编码可带来更高的编码效率,并且ICA可用于揭示自然场景中颜色信息的潜在统计特性。