Color constancy is the ability to infer stable material colors despite changes in lighting, and it is typically ad- dressed computationally using a single image as input. In many recognition and retrieval applications, we have ac- cess to image sets that contain multiple views of the same object in different environments; we show in this technical report and a related publication [8], that correspondences between these images provide important constraints that can improve color constancy. In this report, we present an- other method to solve the multi-view color constancy prob- lem, the Ratio Method. This method provides a means to recover estimates of underlying surface reflectance based on joint estimation of these surface properties and the illu- minants present in multiple images. In contrast to the multi- view Spatial Correlations method (MVSC), this method can leverage any single image color constancy method as a bootstrap for the multi-view solution. The method ex- ploits image correspondences obtained by various align- ment techniques, and we show examples based on match- ing local region features. Our results show that the Ra- tio Method performs similarly to the MVSC method, both of which are improvements over a baseline single-view method.