Image Segmentation Method Based on Spatial Position Information

Abstract

The invention discloses an image segmentation method based on spatial location information. The method includes the follow steps: reading an image, dividing the image into small regions that have fixed sizes and do not overlap mutually, and determining the category number K of the image segmentation; making pixels in each small region come from the same category content, determining the observation value and the likelihood function of joint probability based on the observation value; solving the likelihood function with an expectation maximization (EM) algorithm; comparing the entropy value of posterior probability in each small regionHxwith the threshold value Hx-bar , and splitting the small region into four equal smaller regions if Hx is greater than Hx-bar; checking the adjacent small regions without division, combining the small regions if the categories of the small regions are the same, until no small regions can be combined to obtain a new small regional division; repeating the step two to the step six, until no small areas suitable for division again exist; and outputting the image according to each pixel in the image category label. The divided regions have good integrity and smoothness according to the invention.

Description

technical field [0001] The invention belongs to the field of mixed model image segmentation, in particular to an image segmentation method limited by spatial position based on spatial position information. Background technique [0002] Among many image segmentation methods, clustering methods based on pixel statistics can often obtain stable segmentation results. Among them, the Gaussian mixture model is the most representative clustering method, and the expectation maximization (ExpectationMaximization, EM) algorithm provides a simple and effective maximum likelihood iterative estimation method for model parameters. However, the finite mixture model is directly applied to image segmentation on the premise of the independent assumption of pixels. This segmentation method only considers the statistical characteristics of pixels, but does not consider the spatial position information between pixels. category dependencies. Since pixels with the same intensity distribution may...

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Problems solved by technology

However, this implicit MRF makes the model unable to perform EM calculations directly. Generally, the pseudo-likelihood is used instead of the normal likelihood function. Even so, the EM step cannot obtain a closed-form solution. In the EM step, the Iterative optimization algorithms such as ICM (iterated conditional modes) need to be used

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