We are pleased to announce the publication of an open-access paper demonstrating a novel method for categorizing images using the statistics of their edge co-occurrences. The success of this low-level method in matching both correct and incorrect classifications by humans suggests a reinterpretation of longstanding claims for rapid hierarchical processing in the visual cortex. Laurent U. Perrinet Institut de Neurosciences de la Timone Aix Marseille Universite, CNRS James A. Bednar Institute for Adaptive and Neural Computation University of Edinburgh _______________________________________________________________________________ Laurent U. Perrinet and James A. Bednar. Edge co-occurrences can account for rapid categorization of natural versus animal images. Scientific Reports, Nature Publishing Group, 5:11400, 2015. http://dx.doi.org/10.1038/srep11400 Abstract: Making a judgment about the semantic category of a visual scene, such as whether it contains an animal, is typically assumed to involve high-level associative brain areas. Previous explanations require progressively analyzing the scene hierarchically at increasing levels of abstraction, from edge extraction to mid-level object recognition and then object categorization. Here we show that the statistics of edge co-occurrences alone are sufficient to perform a rough yet robust (translation, scale, and rotation invariant) scene categorization. We first extracted the edges from images using a scale-space analysis coupled with a sparse coding algorithm. We then computed association for different categories (natural, man-made, or containing an animal) by computing the statistics of edge co-occurrences. These differed strongly, with animal images having more curved configurations. We show that this geometry alone is sufficient for categorization, and that the pattern of errors made by humans is consistent with this procedure. Because these statistics could be measured as early as the primary visual cortex, the results challenge widely held assumptions about the flow of computations in the visual system. The results also suggest new algorithms for image classification and signal processing that exploit correlations between low-level structure and the underlying semantic category. -- The University of Edinburgh is a charitable body, registered in Scotland, with registration number SC005336.