How do infants segment objects from the complex visual environment? Investigations of figure-ground perception have been dominated by studies assessing infants' sensitivity to depth and figure cues; few studies have assessed what information infants' use to perceive figures as separate from grounds. Research examining word segmentation suggests that statistical learning might aid segmentation in visual scenes. Despite the numerous studies investigating figure-ground segmentation, none have investigated the role of spatial transitional probabilities as a means for segmentation. To examine this question, we used a habituation/familiarity-preference procedure to assess whether background variability enables 5.5-month-old infants' perception of figures as separate from the background. The results of Experiments 1 and 2 indicated that statistical learning extends to scene segmentation, scene contexts allowed extraction of statistical distribution. Experiment 3 demonstrated that matching the configuration of visual arrays during training and test is essential; mismatched stimuli impede the measurement of segmentation.