Mapping and localization are fundamental problems in autonomous robotics. Autonomous robots need to know where they are in their area of operation to navigate through it and to perform activities of interest. In this paper, we propose an Image-Based Global Localization (VibGL) system that uses Virtual Generalizing Random Access Memory Weightless Neural Networks (VG-RAM WNN). For mapping, we employ a VG-RAM WNN that learns the world positions associated with the images captured along a trajectory. During the localization, new images from the trajectory are presented to the VG-RAM WNN, which outputs their positions in the world. We performed experiments with our VibGL system applied to the problem of localizing an autonomous car. Our experimental results show that the system is able to learn large maps (several kilometers in length) of real world environments and perform global localization with median pose precision of about 3m. Considering a tolerance of 10m VibGL is able to localize the car 95% of the time.