Title :
Image segmentation by tree-structured Markov random fields
Author :
Poggi, Giovanni ; Ragozini, Arturo R P
Author_Institution :
Dipt. di Ingegneria Elettronica e delle Telecommun., Naples Univ., Italy
fDate :
7/1/1999 12:00:00 AM
Abstract :
We propose a new algorithm, based on a tree-structured Markov random field (MRP) model, to carry out the unsupervised classification of images. It presents several appealing features; due to the MRF model, it takes into account spatial dependencies, yet is computationally light because only binary MRFs are used and a progressive refinement of information takes place. Moreover, it is adaptive to the local characteristics of the image and provides useful side information about the segmentation process.
Keywords :
hidden Markov models; image classification; image segmentation; random processes; trees (mathematics); unsupervised learning; MRF model; algorithm; binary MRF; image segmentation; local characteristics; side information; tree-structured Markov random fields; unsupervised image classification; Classification tree analysis; Computational complexity; Context modeling; Convergence; Image classification; Image segmentation; Layout; Markov random fields; Parameter estimation; Signal processing algorithms;
Journal_Title :
Signal Processing Letters, IEEE
