Structured learning of tree potentials in CRF for image segmentation

• Main Authors: Liu, Fayao, Lin, Guosheng, Qiao, Ruizhi, Shen, Chunhua
• Other Authors: School of Computer Science and Engineering
• Format: Article
• Language: English
• Published: 2019
• Subjects: Engineering::Computer science and engineering; Decision Trees; Conditional Random Fields
• Online Access: https://hdl.handle.net/10356/107576; http://hdl.handle.net/10220/50327; http://dx.doi.org/10.1109/TNNLS.2017.2690453
• Institution: Nanyang Technological University

We propose a new approach to image segmentation, which exploits the advantages of both conditional random fields (CRFs) and decision trees. In the literature, the potential functions of CRFs are mostly defined as a linear combination of some predefined parametric models, and then, methods, such as s...