基于KPCA和ANFIS的色彩校正

Color Calibration Based on KPCA and ANFIS

提出结合核主成分分析(KPCA)和自适应神经模糊推理系统(ANFIS)的色彩校正(KPCA_ANFIS)算法.首先将数据通过核函数映射到高维空间,再通过KPCA提取主成分,最后通过ANFIS学习达到色彩校正的目的.实验结果验证了ANFIS用于色彩校正的可行性和有效性,KPCA_ANFIS算法的精度和鲁棒性均优于传统ANFIS.对测试数据(训练数据)的平均误差、最大误差和标准差较传统ANFIS分别下降了37%(45%),34%(40%)和35%(40%).

An algorithm for color calibration was proposed by integrating an ANFIS （ adaptive-networkbased fuzzy inference system） with KPCA （kernel principal component analysis）. The algorithm maps the input data into a higher dimensional feature space with a kernel function first, then extracts principal components of the mapped data by the PCA, and finally implements color calibration by the ANFIS. Experimental results indicate that the proposed algorithm is feasible and effective, and is superior to the conventional ANFIS in both precision and robustness. The average error, maximum error and the error standard deviation regarding the test data （training data） decrease by 37% （45%）, 34% （40%） and 35% （40%）, respectively.