Design of the color classification system for sunglass lenses using PCA-PSO-ELM. (15th February 2022) - Jian, He; Lin, Qifeng; Wu, Juntao; Fan, Xianguang; Wang, Xin
Highlights: We propose a new color classification system for sunglass lenses. We built a data collection system to collect the spectral reflectance data of the sunglasses. We propose a classification algorithm based on PCA, PSO and ELM. We verify the effectiveness and the feasibility of the algorithms by processing the spectral reflectance data of sunglasses. Abstract: Color deviation of the sunglass lens brings many problems to the pairing of sunglasses. In order to accurately classify the sunglass lens by color depth, a data acquisition system based on spectral analysis method is developed, which is composed of reflection integrating sphere, optical fiber spectrometer and optical fiber. Besides, the classification algorithm based on Principal Component Analysis, with Particle Swarm Optimization and Extreme Learning Machine is proposed. In which, PCA reduces the dimensions of the spectral reflectance data, PSO optimizes the input weights and hidden layer bias values of ELM, and the optimized ELM obtains a satisfactory classification through certain learning and training. This algorithm avoids the lengthy formula calculations in the traditional color classification method, and requires fewer hidden layer neurons to achieve high and stable classification accuracy in ELM. The classification accuracy of PCA-PSO-ELM and PCA-ELM, LM-BP, LS-SVM is compared by the experiments. It is proved that the adoption of the proposed PCA-PSO-ELM in the color classification of sunglass lenses is feasible and effective.