The problem of foreground/background segmentation is of great importance in image processing and computer vision. We present a novel Linear-Programming (LP)-based algorithm for color image segmentation. This algorithm segments an image into a conceptually-meaningful foreground region (usually corresponding to the object of interest) and background regions. From a few user specified strokes we learn two Gaussian Mixture models corresponding to the foreground and background region respectively. The algorithm performs well even when the object region consists of several different colors and textures. Due to the global optimality of LP, our algorithm is free from the drawback of getting into local minima.
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