A Color Image Segmentation Method Based on Spatial Dirichlet Mixture Model

Abstract

The invention relates to a color image segmentation method based on a space Dirichlet hybrid model. A provided statistic model is based on the Dirichlet hybrid model; and Dirichlet distribution is a multi-element generalized Beta distribution, and can comprise symmetrical and asymmetric modes compared with other distributions (such as Gaussian distribution), so that the method can flexibly process various images and embody high segmentation accuracy. Spatial neighborhood characteristics are integrated into the Dirichlet hybrid model, thereby embodying better robustness; in the process of solving parameters of the Dirichlet hybrid model, a method based on variational Bayesian inference is adopted to enable model solving to be more accurate and efficient; and the defects, that it is generally easy for methods in the prior art to be trapped into local minimum and iteration times is too much, of maximum likelihood estimation (ML) and maximum posterior probability estimation (MAP) adopted in the methods in the prior art are overcome.

Description

technical field [0001] The invention relates to the field of computer image analysis and processing, in particular to a color image segmentation method based on a spatial Dirichlet mixture model. Background technique [0002] In recent years, with the advent of the information age, especially the digital age, image segmentation technology has been widely used in medicine, military engineering and other fields. Image segmentation technology can facilitate the analysis of various quantitative and qualitative image data obtained, so that these data can be better applied to engineering practice. For example, in medicine, new medical imaging technologies such as computed tomography (CT: Computed Tomography), magnetic resonance imaging (MRI: Magnetic Resonance Imaging), ultrasound (US: Ultrasonography) have been widely used in medical diagnosis, preoperative planning, treatment, surgery, etc. Post-monitoring and other links. [0003] Image segmentation is a key step in image ana...

AI Technical Summary

Problems solved by technology

Compared with HMRF-EM, this method has stronger robustness, so the segmentation effect is further improved, but the algorithm does not strictly follow the gradient descent in the process of minimizing the objective function, which leads to too many iterations and increases the complexity of calculation. sex

[0006] In the field of image segmentation, it is necessary to consider how to solve the two problems of high noise and high computational overhead, so proposing a robust and simple and effective algorithm is one of the key research directions in the field of image segmentation today.

Images