Abstract

We describe a new approach for learning to perform class- based segmentation using only unsegmented training examples. As in previous methods, we first use training images to extract fragments that contain common ob ject parts. We then show how these parts can be segmented into their figure and ground regions in an automatic learning process. This is in contrast with previous approaches, which required complete manual segmentation of the ob jects in the training examples. The figure-ground learning combines top-down and bottom-up processes and proceeds in two stages, an initial approximation followed by iterative refinement. The initial approximation produces figure-ground labeling of individual image fragments using the unsegmented training images. It is based on the fact that on average, points inside the ob ject are cov- ered by more fragments than points outside it. The initial labeling is then improved by an iterative refinement process, which converges in up to three steps. At each step, the figure-ground labeling of individual fragments produces a segmentation of complete ob jects in the training images, which in turn induce a refined figure-ground labeling of the in- dividual fragments. In this manner, we obtain a scheme that starts from unsegmented training images, learns the figure-ground labeling of image fragments, and then uses this labeling to segment novel images. Our ex- periments demonstrate that the learned segmentation achieves the same level of accuracy as methods using manual segmentation of training im- ages, producing an automatic and robust top-down segmentation.