Pseudo-likelihood equations for Potts model on higher-order neighborhood systems: A quantitative approach for parameter estimation in image analysis

Autor(es)

Levada, Alexandre L. M. ^[1] ; Mascarenhas, Nelson D. A. ^[2] ; Tannus, Alberto ^[1]

Afiliação do(s) autor(es)

^[1] Univ Sao Paulo, Inst Fis Sao Carlos, BR-13560970 Sao Carlos, SP - Brazil

^[2] Univ Fed Sao Carlos, Dept Comp, BR-13565905 Sao Carlos, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: BRAZILIAN JOURNAL OF PROBABILITY AND STATISTICS; v. 23, n. 2, p. 120-140, DEC 2009.
• Citações Web of Science: 3

Resumo

This paper presents analytical pseudo-likelihood (PL) equations for Potts Markov random field (MRF) model parameter estimation on higher-order neighborhood systems by expanding the derivative of the log-PL function based on the enumeration of all possible contextual configuration patterns given a neighborhood system. The proposed equations allow the modeling of less restrictive neighborhood systems in a large number of MRF applications in a computationally feasible way. To evaluate the proposed estimation method we propose a hypothesis testing approach, derived by approximating the asymptotic variance of MPL parameter estimators using the observed Fisher information. The definition of the asymptotic variance, together with the test size alpha and p-values, provide a complete framework for quantitative analysis. Experiments with synthetic images generated by Markov chain Monte Carlo simulation methods assess the accuracy of the proposed estimation method, indicating that higher-order neighborhood systems reduce the MPL estimator asymptotic variance and improve estimation performance. (AU)

• Processo FAPESP: 06/01711-4 - Combinação de modelos de campos aleatórios markovianos para classificação contextual de imagens multiespectrais
• Beneficiário: Alexandre Luís Magalhães Levada
• Modalidade de apoio: Bolsas no Brasil - Doutorado