Learning to Combine Bottom-Up and Top-Down Segmentation

• Published in: International journal of computer vision Vol. 81; no. 1; pp. 105 - 118
• Main Authors: Levin, Anat, Weiss, Yair
• Format: Journal Article
• Language: English
• Published: Boston Springer US 01.01.2009; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Computer Imaging; Computer Science; Image Processing and Computer Vision; Pattern Recognition; Pattern Recognition and Graphics; Studies; Vision; Image segmentation; Object detection
• DOI: 10.1007/s11263-008-0166-0
• ISSN: 0920-5691

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-down algorithms often require hundreds of fragments, our simultaneous learning procedure yields algorithms with a handful of fragments that are combined with low-level cues to efficiently compute high quality segmentations.