In this paper, we present a multi-scale approach based on superpixel classification for optic cup localization. Our approach provides 3 major contributions. First, a contrast enhancement scheme is proposed to reduce illumination influence and enhance feature discrimination. Second, features are extracted from multiple superpixels scales for richer description of the optic cup. Third, a unique cup is localized by integrating the multi-scales together using majority voting. Our approach was validated on a clinical online dataset, ORIGA-light, of 650 population-based images. Overall, our approach is able to achieve a 0.248 non-overlap ratio (m1) and a 0.085 absolute CDR error (δ). Experimental results also shows that our multi-scale approach has a complementary effect to increase performance stability, and is able to achieve a higher accuracy when compared with the previous state-of-the-art superpixel-based method.