Year: 2015
Type: preprint
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.
Source: arXiv (Cornell University)
Author: Masatran, Rajasekaran
Institution: Indian Institute of Technology Madras
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2402093908