11,570 research outputs found
Estimating the granularity coefficient of a Potts-Markov random field within an MCMC algorithm
This paper addresses the problem of estimating the Potts parameter B jointly
with the unknown parameters of a Bayesian model within a Markov chain Monte
Carlo (MCMC) algorithm. Standard MCMC methods cannot be applied to this problem
because performing inference on B requires computing the intractable
normalizing constant of the Potts model. In the proposed MCMC method the
estimation of B is conducted using a likelihood-free Metropolis-Hastings
algorithm. Experimental results obtained for synthetic data show that
estimating B jointly with the other unknown parameters leads to estimation
results that are as good as those obtained with the actual value of B. On the
other hand, assuming that the value of B is known can degrade estimation
performance significantly if this value is incorrect. To illustrate the
interest of this method, the proposed algorithm is successfully applied to real
bidimensional SAR and tridimensional ultrasound images
Blending Learning and Inference in Structured Prediction
In this paper we derive an efficient algorithm to learn the parameters of
structured predictors in general graphical models. This algorithm blends the
learning and inference tasks, which results in a significant speedup over
traditional approaches, such as conditional random fields and structured
support vector machines. For this purpose we utilize the structures of the
predictors to describe a low dimensional structured prediction task which
encourages local consistencies within the different structures while learning
the parameters of the model. Convexity of the learning task provides the means
to enforce the consistencies between the different parts. The
inference-learning blending algorithm that we propose is guaranteed to converge
to the optimum of the low dimensional primal and dual programs. Unlike many of
the existing approaches, the inference-learning blending allows us to learn
efficiently high-order graphical models, over regions of any size, and very
large number of parameters. We demonstrate the effectiveness of our approach,
while presenting state-of-the-art results in stereo estimation, semantic
segmentation, shape reconstruction, and indoor scene understanding
- …