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Maximum Likelihood Estimation Methods for Multispectral Random Field Image Models
June 1999 (vol. 21 no. 6)
pp. 537-543

Abstract—This work considers the problem of estimating parameters of multispectral random field (RF) image models using maximum likelihood (ML) methods. For images with an assumed Gaussian distribution, analytical results are developed for multispectral simultaneous autoregressive (MSAR) and Markov random field (MMRF) models which lead to practical procedures for calculating ML estimates. Although previous work has provided least squares methods for parameter estimation, the superiority of the ML method is evidenced by experimental results provided in this work. The effectiveness of multispectral RF models using ML estimates in modeling color texture images is also demonstrated.

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Index Terms:
Maximum likelihood estimation, multispectral image models, color texture models, multispectral random fields, multispectral autoregressive models, multispectral Markov models.
Citation:
Jesse Bennett, Alireza Khotanzad, "Maximum Likelihood Estimation Methods for Multispectral Random Field Image Models," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 21, no. 6, pp. 537-543, June 1999, doi:10.1109/34.771322
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