For Full-Text PDF, please login, if you are a member of IEICE,|
or go to Pay Per View on menu list, if you are a nonmember of IEICE.
Markov Random Field Based Image Labeling with Parameter Estimation by Error Backpropagation
Il Young KIM Hyun Seung YANG
IEICE TRANSACTIONS on Information and Systems
Publication Date: 1991/10/25
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Image Processing, Computer Graphics and Pattern Recognition
Full Text: PDF>>
Image labeling is a process of recognizing each segmented region properly exploiting the properties of the regions and the spatial relationsships between regions. In some sense, image labeling is an optimization process of indexing regions using the constraints as to the scene knowledge. In this paper, we further investigate a method of efficiently labeling images using the Markov Random Field (MRF). MRF model is defined on the region adjacency graph and the labeling is then optimally determined using the simulated annealing. To endow the adaptability to the MRF-based image labeling, we have proposed a parameter estimation technique based on error backpropagation. We analyze the proposed method through experiments using the real natural scene images.