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Efficient piecewise learning for conditional random fields

Efficient piecewise learning for conditional random fields,10.1109/CVPR.2010.5540123,Karteek Alahari,Christopher Russell,Philip H. S. Torr

Efficient piecewise learning for conditional random fields   (Citations: 3)
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Conditional Random Field models have proved effec- tive for several low-level computer vision problems. Infer- ence in these models involves solving a combinatorial op- timization problem, with methods such as graph cuts, be- lief propagation. Although several methods have been pro- posed to learn the model parameters from training data, they suffer from various drawbacks. Learning these pa- rameters 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 mar- gin estimation. Iterative solutions have been proposed to solve the resulting convex optimization problem. Each iter- ation involves solving an inference problem over all the la- bels, which limits the efficiency of these structured methods. In this paper we present an efficient large margin piece- wise 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 con- straints, and solve it using an efficient scheme. Our method is both memory and computationally efficient. We show re- sults on publicly available standard datasets.
Conference: Computer Vision and Pattern Recognition - CVPR , pp. 895-901, 2010
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    • ...∂wn . However, exact computation of these gradients is infeasible, because it would require vast computational effort to calculate the gradient marginalization of the partition function Z = i∈V Zi. Consequently, an efficient piecewise training technique [16] is adopted for parameter learning...
    • ...More details of the piecewise training technique can be found in [16]...

    Xi Liet al. Superpixel-based object class segmentation using conditional random fi...

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