A finite mixture model for image segmentation

Authors:
Marco Alfò;Luciano Nieddu;Donatella Vicari
Affiliations:
Dipartimento di Statistica, Probabilità e Statistiche Applicate, Sapienza Università di Roma, Rome, Italy;Facoltà di Economia, Libera Università "S. Pio V" di Roma, Rome, Italy;Dipartimento di Statistica, Probabilità e Statistiche Applicate, Sapienza Università di Roma, Rome, Italy
Venue:
Statistics and Computing
Year:
2008

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Abstract

In this paper, we propose a model for image segmentation based on a finite mixture of Gaussian distributions. For each pixel of the image, prior probabilities of class memberships are specified through a Gibbs distribution, where association between labels of adjacent pixels is modeled by a class-specific term allowing for different interaction strengths across classes. We show how model parameters can be estimated in a maximum likelihood framework using Mean Field theory. Experimental performance on perturbed phantom and on real benchmark images shows that the proposed method performs well in a wide variety of empirical situations.