The June issue of IEEE Computer Magazine features an article by myself and Arnold Smeulders titled “Visual-Concept Search Solved?“, which is available for download here. Interpreting the visual signal that enters the brain is an amazingly complex task, deeply rooted in life experience. Approximately half the brain is engaged in assigning a meaning to the incoming image, starting with the categorization of all visual concepts in the scene. Nevertheless, during the past five years, the field of computer vision has made considerable progress. It has done so not on the basis of precise modeling of all encountered objects and scenes—that task would be too complex and exhaustive to execute—but on the basis of combining rich, sensory-invariant descriptions of all patches in the scene into semantic classes learned from a limited number of examples. Research has reached the point where one part of the community suggests visual search is practically solved and progress has only been incremental, while another part argues that current solutions are weak and generalize poorly. We’ve done an experiment to shed light on the issue. Contrary to the widespread belief that visual-search progress is incremental and detectors generalize poorly, our experiment shows that progress has doubled on both counts in just three years. These results suggest that machine understanding of images is within reach.