Supervised Color Correction Based on QPSO-BP Neural Network Algorithm

Color information is very important for the applications of object recognition and image retrieval. However, the actual color varies by the illumination conditions. A supervised color correction based on hybrid algorithm combining Quantum Particle Swarm Optimization (QPSO) with Back Propagation (BP) neural network is proposed in this paper to reduce the effects of illumination conditions. Firstly, the Macbeth color checker containing 24 color patches is adopted. Then those color values of color patches under unknown illumination and standard illumination are recorded in order to obtain the learning samples. Finally, the transformation model is established by QPSO-BP neural network algorithm according to the learning samples. The experimental results show that the QPSO-BP algorithm is better than BP algorithm in convergence speed. Comparably, the proposed algorithm has better color correction result, thus can be efficiently applied in practice.