  Boosting is a method for learning a single accurate predictor by linearly
combining a set of less accurate weak learners. Recently, structured learning
has found many applications in computer vision. Inspired by structured support
vector machines (SSVM), here we propose a new boosting algorithm for structured
output prediction, which we refer to as StructBoost. StructBoost supports
nonlinear structured learning by combining a set of weak structured learners.
As SSVM generalizes SVM, our StructBoost generalizes standard boosting
approaches such as AdaBoost, or LPBoost to structured learning. The resulting
optimization problem of StructBoost is more challenging than SSVM in the sense
that it may involve exponentially many variables and constraints. In contrast,
for SSVM one usually has an exponential number of constraints and a
cutting-plane method is used. In order to efficiently solve StructBoost, we
formulate an equivalent $ 1 $-slack formulation and solve it using a
combination of cutting planes and column generation. We show the versatility
and usefulness of StructBoost on a range of problems such as optimizing the
tree loss for hierarchical multi-class classification, optimizing the Pascal
overlap criterion for robust visual tracking and learning conditional random
field parameters for image segmentation.
