Dirichlet Gaussian mixture model: Application to image segmentation

摘要

Gaussian mixture model based on the Dirichlet distribution (Dirichlet Gaussian mixture model) has recently received great attention for modeling and processing data. This paper studies the new Dirichlet Gaussian mixture model for image segmentation. First, we propose a new way to incorporate the local spatial information between neighboring pixels based on the Dirichlet distribution. The main advantage is its simplicity, ease of implementation and fast computational speed. Secondly, existing Dirichlet Gaussian model uses complex log-likelihood function and require many parameters that are difficult to estimate. The total parameters in the proposed model lesser and the log-likelihood function have a simpler form. Finally, to estimate the parameters of the proposed Dirichlet Gaussian mixture model, a gradient method is adopted to minimize the negative log-likelihood function. Numerical experiments are conducted using the proposed model on various synthetic, natural and color images. We demonstrate through extensive simulations that the proposed model is superior to other algorithms based on the model-based techniques for image segmentation.