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- W2100081703 abstract "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." @default.
- W2100081703 created "2016-06-24" @default.
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- W2100081703 date "2010-01-01" @default.
- W2100081703 modified "2023-10-16" @default.
- W2100081703 title "StyP-Boost: A Bilinear Boosting Algorithm for Learning Style-Parameterized Classifiers" @default.
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- W2100081703 doi "https://doi.org/10.5244/c.24.60" @default.
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