Title :
Learning optimized MAP estimates in continuously-valued MRF models
Author :
Samuel, Kegan G G ; Tappen, Marshall F
Author_Institution :
Sch. of Electr. Eng. & Comput. Sci., Univ. of Central Florida, Orlando, FL, USA
Abstract :
We present a new approach for the discriminative training of continuous-valued Markov Random Field (MRF) model parameters. In our approach we train the MRF model by optimizing the parameters so that the minimum energy solution of the model is as similar as possible to the ground-truth. This leads to parameters which are directly optimized to increase the quality of the MAP estimates during inference. Our proposed technique allows us to develop a framework that is flexible and intuitively easy to understand and implement, which makes it an attractive alternative to learn the parameters of a continuous-valued MRF model. We demonstrate the effectiveness of our technique by applying it to the problems of image denoising and in-painting using the Field of Experts model. In our experiments, the performance of our system compares favourably to the Field of Experts model trained using contrastive divergence when applied to the denoising and in-painting tasks.
Keywords :
Markov processes; image denoising; random processes; continuous-valued MRF model; continuous-valued Markov random field; continuously-valued MRF models; contrastive divergence; discriminative training; field of experts model; image denoising; in-painting; minimum energy solution; optimized MAP estimates; Computer science; Energy measurement; Image denoising; Loss measurement; Machine vision; Markov random fields; Maximum likelihood estimation; Noise reduction; Parameter estimation; Stochastic processes;
Conference_Titel :
Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on
Conference_Location :
Miami, FL
Print_ISBN :
978-1-4244-3992-8
DOI :
10.1109/CVPR.2009.5206774