Image Segmentation Method Based on Spatially Restricted Neighborhood Mixture Model

Abstract

The invention discloses an image segmentation method based on a space limitation neighborhood hybrid model, belongs to the field of images, and solves the problems of coupling in working out hybrid model parameters by an EM (expectation maximization) algorithm during image segmentation at present. The method comprises the following steps: 1, creating the space limitation neighborhood hybrid model according to an independent hybrid model, namely firstly, selecting a neighborhood in each pixel position of the independent hybrid model and determining and selecting a model component by prior probabilities in the neighborhoods; secondly, generating a set of observation values in the neighborhood corresponding to the pixel position by the determined model component; finally, obtaining a likelihood function of the space limitation neighborhood hybrid model according to the determined model component and the generated observation values; 2, working out the model parameters of the space limitation neighborhood hybrid model by the visual observation values of the pixels of the image; 3, obtaining segmented images by the obtained model parameters of the space limitation neighborhood hybrid model. The method is used for image segmentation.

Description

technical field [0001] The invention belongs to the field of image processing. Background technique [0002] Image segmentation is the basis of automatic semantic content analysis of images, and the segmentation results often have a profound impact on the classification and recognition in subsequent processing. Image segmentation methods emerge in endlessly. Among the many segmentation algorithms, the clustering algorithm based on the mixture model, especially the Gaussian mixture model (Gaussian mixture models, GMM) has been in a relatively active research state. Since the model parameters can adopt the maximum expected Expectation-Maximization (EM) algorithm (Expectation-Maximization, EM) can be effectively estimated, and EM has the advantages of simple implementation and guaranteed convergence. As a generative model, the segmentation result of the hybrid model has an intuitive probability interpretation, which provides a basis for subsequent analysis of image content. fo...

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Problems solved by technology

[0003] The purpose of the present invention is to solve the coupling problem of using EM algorithm to obtain the mixed model parameters when performing image segmentation at present. The present invention provides an image segmentation method based on the spatially restricted neighborhood mixed model

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