Humans can easily memorize images of places and labels (road names, addresses, etc.) associated with them, as well as trajectories defined by sequences of images and corresponding positions. Later, they are able to remember places' labels and relative positions when seeing the same images again. In this work, we present an image-based mapping, global localization and position tracking system based on Virtual Generalizing Random Access Memory (VG-RAM) weightless neural networks, dubbed VIBML. VIBML mimics humans ability of learning about a place and of recognizing the same place in a later moment, as well as of tracking self-movement through the environment using images. We evaluated the performance of VIBML on the precise localization of an autonomous car using real-world datasets. Our experimental results showed that VIBML is able to localize car-like robots on large maps of real world environments with accuracy equivalent to that of state-of-the-art methods - VIBML is able to localize an autonomous car with average positioning error of 1.12m and with 75% of the poses with error below 1.5m in a 3.75km path around the main campus of the Federal University of Espírito Santo.