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EMMCVPR
2005
Springer

Exploiting Inference for Approximate Parameter Learning in Discriminative Fields: An Empirical Study

9 years 3 months ago
Exploiting Inference for Approximate Parameter Learning in Discriminative Fields: An Empirical Study
Abstract. Estimation of parameters of random field models from labeled training data is crucial for their good performance in many image analysis applications. In this paper, we present an approach for approximate maximum likelihood parameter learning in discriminative field models, which is based on approximating true expectations with simple piecewise constant functions constructed using inference techniques. Gradient ascent with these updates exhibits compelling limit cycle behavior which is tied closely to the number of errors made during inference. The performance of various approximations was evaluated with different inference techniques showing that the learned parameters lead to good classification performance so long as the method used for approximating the gradient is consistent with the inference mechanism. The proposed approach is general enough to be used for the training of, e.g., smoothing parameters of conventional Markov Random Fields (MRFs).
Sanjiv Kumar, Jonas August, Martial Hebert
Added 27 Jun 2010
Updated 27 Jun 2010
Type Conference
Year 2005
Where EMMCVPR
Authors Sanjiv Kumar, Jonas August, Martial Hebert
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