StyP-Boost: A Bilinear Boosting Algorithm for Learning Style-Parameterized Classifiers

We introduce a novel bilinear boosting algorithm, which extends the multi-class boosting framework of JointBoost to optimize a bilinear objective function. This allows style parameters to be introduced to aid classification, where style is any factor which the classes vary with systematically, modeled by a vector quantity. The algorithm allows learning to take place across different styles. We apply this Style Parameterized Boosting framework (StyP-Boost) to two object class segmentation tasks: road surface segmentation and general scene parsing. In the former the style parameters represent global surface appearance, and in the latter the probability of belonging to a scene-class. We show how our framework improves on 1) learning without style, and 2) learning independent classifiers within each style. Further, we achieve state-of-the-art results on the Corel database for scene parsing.

• Type: Journal
• Year: 2010
• Where: BMVC
• Authors: Jonathan Warrell, Philip H. S. Torr, Simon Prince
• Keywords: Bilinear Objective Function, BMVC 2010, Boosting, Computer Vision, Multi-class Boosting Framework