基于旋转主成分分析的颜色组分预测

Color component prediction based on rotated principal component analysis

针对传统主成分分析法在建立光谱色彩空间时所产生的"负指标"问题,提出了一种基于旋转主成分分析的颜色组分预测方法。该方法在最大程度地保持重要主成分对原始多光谱空间信息的累积方差贡献率的同时,将初始特征向量旋转变换为一组可作为实际基础颜色组分的全正向量,这些特征向量中的元素应按列向0或1分化。采用MunsellMatte Collection I光谱数据库中的数据进行了实验,其中96%以上色块的光谱重构误差都控制在可容许的3个色差单位内。实验结果表明:这种新的预测方法不仅能很好地揭示目标图像的真实颜色成分,还具有较高的光谱数据重构精度。

To aiming at the problem of negative index when building a spectral color space by traditional principal component analysis(PCA), a color component prediction method based on Rotated Principal Component Analysis(RPCA) is proposed,which performs the rotating transformation from initial eigen vectors to a set of all-positive vectors as the physical basis color components while retaining the maximum cumulative variance contribution ratio of significant principal components to the original multi-spectral space....