Segmenting meaningful targets from cluttered scenes is a fundamental function of the visual system. Evolution and development have been suggested to optimize the brain's solution to this computationally challenging task by tuning the visual system to features that co-occur frequently in natural scenes (e.g., collinear edges) [1, 2, 3]. However, the role of shorter-term experience in shaping the utility of scene statistics remains largely unknown. Here, we ask whether collinearity is a specialized case, or whether the brain can learn to recruit any image regularity for the purpose of target identification. Consistent with long-term optimization for typical scene statistics, observers were better at detecting collinear contours than configurations of elements oriented at orthogonal or acute angles to the contour path. However, training resulted in improved detection of orthogonal contours that lasted for several months, suggesting retuning rather than transient changes of visual sensitivity. Improvement was also observed for acute contours but only after longer training. These results demonstrate that the brain flexibly exploits image regularities and learns to use discontinuities typically associated with surface boundaries (orthogonal, acute alignments) for contour linking and target identification. Thus, short-term experience in adulthood shapes the interpretation of scenes by assigning new statistical utility to image regularities.