Learning to Segment

Abstract. We describe a new approach for learning to perform classbased segmentation using only unsegmented training examples. As in previous methods, we first use training images to extract fragments that contain common object 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 objects 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 object are covered 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...

• Type: Conference
• Year: 2004
• Where: ECCV
• Authors: Eran Borenstein, Shimon Ullman
• Keywords: Complete Manual Segmentation, Computer Vision, ECCV 2004, Individual Fragments, Individual Image Fragments, Robust Top-down Segmentation, Training Images