A Latent-Variable Lattice Model
Machine Learning
2016-06-09 v7 Computer Vision and Pattern Recognition
Machine Learning
Abstract
Markov random field (MRF) learning is intractable, and its approximation algorithms are computationally expensive. We target a small subset of MRF that is used frequently in computer vision. We characterize this subset with three concepts: Lattice, Homogeneity, and Inertia; and design a non-markov model as an alternative. Our goal is robust learning from small datasets. Our learning algorithm uses vector quantization and, at time complexity O(U log U) for a dataset of U pixels, is much faster than that of general-purpose MRF.
Cite
@article{arxiv.1512.07587,
title = {A Latent-Variable Lattice Model},
author = {Rajasekaran Masatran},
journal= {arXiv preprint arXiv:1512.07587},
year = {2016}
}
Comments
6 pages, with 4 figures, 8 algorithms, and 1 table