Learning to Combine Bottom-Up and Top-Down Segmentation

Bottom-up segmentation based only on low-level cues is a notoriously difficult problem. This difficulty has lead to recent top-down segmentation algorithms that are based on class-specific image information. Despite the success of top-down algorithms, they often give coarse segmentations that can be significantly refined using low-level cues. This raises the question of how to combine both top-down and bottom-up cues in a principled manner. In this paper we approach this problem using supervised learning. Given a training set of ground truth segmentations we train a fragment-based segmentation algorithm which takes into account both bottom-up and top-down cues simultaneously, in contrast to most existing algorithms which train top-down and bottom-up modules separately. We formulate the problem in the framework of Conditional Random Fields (CRF) and derive a feature induction algorithm for CRF, which allows us to efficiently search over thousands of candidate fragments. Whereas pure top...

• Type: Conference
• Year: 2006
• Where: ECCV
• Authors: Anat Levin, Yair Weiss
• Keywords: Bottom-up Cues, Computer Vision, ECCV 2006, Low-level Cues, Pure Top-down Algorithms, Top-down Cues, Top-down Segmentation Algorithms