Illumination estimation in complex and multi-illumination scenes is a difficult and hot point in computational color constancy field. An illumination estimation algorithm based on exemplar learning in the logarithm domain is proposed. The effects of illumination on chrominance of an image are studied, and the log-chrominance histogram is extracted as the illumination consistency feature. The frame of exemplar learning is introduced, and the illumination of target scenes is estimated by known-illumination exemplars with similar features. Image segmentation is applied by the algorithm firstly, then illumination estimation is performed for each segment independently, and segmental illuminations are fused together to calculate the illumination for the whole image. Experiments are carried out on several single illumination and multiple illumination data sets. The experimental results show that compared with other advance methods, the proposed method reduces the median error of the illumination estimation by 5%-14% with higher accuracy and higher robustness.