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| Title: | Unsupervised Image Segmentation using Tabu Search and Hidden Markov Random Field Model and Hidden Markov Random Field Model |
| Authors: | Nanda, P K
Patra, D
Pradhan, A |
| Issue Date: | 2004 |
| Publisher: | National Institute of Technology, Rourkela, India |
| Citation: | National Conference on Recent Advances in Power, Signal Processing and Control, Nov 17-18, 2004, Rourkela India, P 133-139 |
| Abstract: | We propose a Tabu search based Expectation
Maximization (EM) algorithm for image segmentation
in an unsupervised frame work. Hidden Markov
Random Field (HMRF) model is used to model the
images. The observed image is considered to be a
realization of Gaussian Hidden Markov Random Field
(GHMRF) model. The segmentation problem is
formulated as a pixel labeling problem. The GHMRF
model parameters as well as the image labels are
assumed to be unknown. This incomplete data problem
is solved using the notions of expectation maximization.
The expectation step obtains the MAP estimate of the
image labels, assuming the availability of parameter
estimates. This is achieved by the proposed Tabu
Search Algorithm. The estimated image labels are used
to obtain the estimates of parameters in the
maximization step. Eventually, the EM algorithm
converges to the desired labelization. Our algorithm
does not require the proper initial estimates of the
parameters. Simulation results are p... |
| Description: | Copyright for this article belongs to the publisher of the proceedings |
| URI: | http://hdl.handle.net/2080/360 |
| Appears in Collections: | Conference Papers
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| pknanda_dpatra_APSC04_133-139.pdf | | 257Kb | Adobe PDF | View/Open |
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