StyP-Boost: a bilinear boosting algorithm for learning style-parameterized classifiers

Conference item

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.

Authors: Warrell, J; Prince, S; Torr, P

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: British Machine Vision Association and Society for Pattern Recognition
• Host title: Proceedings of the British Machine Vision Conference, BMVC 2010
• Publication date: 2010-01-01
• Event title: British Machine Vision Conference 2010 (BMVC2010)
• Event location: Aberystwyth
• Event website: https://bmva-archive.org.uk/bmvc/2010/index.html
• Event start date: 2010-08-31
• Event end date: 2010-09-03
• ISBN: 1901725405
• Language: English