Paper Info
Title
Parallel image classification using multiscale Markov random fields
Abstract
In this paper, we are interested in massively parallel multiscale relaxation algorithms applied to image classification. First, we present a classical multiscale model applied to supervised image classification. The model consists of a label pyramid and a whole observation field. The potential functions of the coarse grid are derived by simple computations. Then, we propose another scheme introducing a local interaction between two neighbor grids in the label pyramid. This is a way to incorporate cliques with far apart sites for a reasonable price. Finally we present the results on noisy synthetic data and on a SPOT image obtained by different relaxation methods using these models.
Year
DOI
Venue
1993
10.1109/ICASSP.1993.319766
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: image and multidimensional signal processing - Volume V
Keywords
Field
DocType
Markov processes,hierarchical systems,image recognition,parallel algorithms,relaxation theory,SPOT image,cliques,image classification,label pyramid,massively parallel multiscale relaxation algorithms,multiscale Markov random fields
Random field,Pattern recognition,Parallel algorithm,Massively parallel,Computer science,Markov chain,Synthetic data,Artificial intelligence,Pyramid,Contextual image classification,Grid
Conference
Volume
ISBN
Citations 
5
0-7803-0946-4
11
PageRank 
References 
Authors
3.92
2
3
Name
Order
Citations
PageRank
Zoltan Kato126528.28
Marc Berthod2429163.29
Josiane Zerubia32032232.91