A Meta-Learning Approach to Conditional Random Fields Using Error-Correcting Output Codes

—We present a meta-learning framework for the design of potential functions for Conditional Random Fields. The design of both node potential and edge potential is formulated as a generic classification problem where margin classifiers are used. The set of state transitions for the edge potential is treated as a set of different classes, thus defining a multi-class learning problem. The Error-Correcting Output Codes (ECOC) technique is used to deal with the multi-class problem. Furthermore, the point defined by the combination of margin classifiers in the ECOC space is interpreted in a probabilistic manner, and the obtained distance values are then converted into potential values. The proposed model exhibits very promising results when used in two real detection problems. Keywords-Conditional Random Fields; Error-Correcting Output Codes; Segmentation; Intravascular Ultrasound

• Type: Conference
• Year: 2010
• Where: ICPR
• Authors: Francesco Ciompi, Oriol Pujol, Petia Radeva
• Keywords: Computer Vision, Edge Potential, Error-Correcting Output Codes, ICPR 2010, Margin Classifiers