Conditional Random Field models have proved effective for several low-level
computer vision problems. Inference in these models involves solving a
combinatorial optimization problem, with methods such as graph cuts, belief
propagation. Although several methods have been proposed to learn the model
parameters from training data, they suffer from various drawbacks. Learning
these parameters involves computing the partition function, which is
intractable. To overcome this, state-of-the-art structured learning methods
frame the problem as one of large margin estimation. Iterative solutions have
been proposed to solve the resulting convex optimization problem. Each
iteration involves solving an inference problem over all the labels, which
limits the efficiency of these structured methods. In this paper we present an
efficient large margin piecewise learning method which is widely applicable. We
show how the resulting optimization problem can be reduced to an equivalent
convex problem with a small number of constraints, and solve it using an
efficient scheme. Our method is both memory and computationally efficient. We
show results on publicly available standard datasets.