Auto-context and its application to high-level vision tasks

The notion of using context information for solving highlevel vision problems has been increasingly realized in the field. However, how to learn an effective and efficient context model, together with the image appearance, remains mostly unknown. The current literature using Markov Random Fields (MRFs) and Conditional Random Fields (CRFs) often involves specific algorithm design, in which the modeling and computing stages are studied in isolation. In this paper, we propose an auto-context algorithm. Given a set of training images and their corresponding label maps, we first learn a classifier on local image patches. The discriminative probability (or classification confidence) maps by the learned classifier are then used as context information, in addition to the original image patches, to train a new classifier. The algorithm then iterates to approach the ground truth. Auto-context learns an integrated low-level and context model, and is very general and easy to implement. Under near...

• Type: Conference
• Year: 2008
• Where: CVPR
• Authors: Zhuowen Tu
• Keywords: Auto-context Algorithm, Computer Vision, Conditional Random Fields, CVPR 2008, Efficient Context Model, Markov Random Fields, Specific Algorithm Design