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Learning Image Segmentations from Experience

The Problem: The image segmentation problem originated from the Gestalt school of psychological research, which focused on the organizational and grouping principles of human vision. Segmentation algorithms divide images into regions based on statistical or aesthetic qualities. Common goals are to encode the image efficiently, to separate foreground figures from background, or to locate the boundaries of objects in the image. Previous segmentation algorithms include the snakes, or contour, method by Kass, et al. [3], minimum description length regions by LeClerc [4], and normalized graph-cutting by Shi and Malik [6].