Texture image segmentation method based on independent Gaussian hybrid model

A Gaussian mixture model and texture image technology, applied in the field of image processing, can solve the problems of poor segmentation result robustness and initialization sensitivity, and achieve the effect of improving segmentation robustness

Inactive Publication Date: 2009-09-23
XIDIAN UNIV
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However, this type of method is sensitive to initialization due to the use of EM a...

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  • Texture image segmentation method based on independent Gaussian hybrid model
  • Texture image segmentation method based on independent Gaussian hybrid model
  • Texture image segmentation method based on independent Gaussian hybrid model

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[0022] refer to figure 1 , the specific implementation steps of the present invention are as follows:

[0023] Step 1: Simultaneously perform 3-layer wavelet transform on the training texture image, 3-layer dual-tree complex wavelet transform, that is, DTCWT and 3-layer Contourlet transform, and extract the required 15 features on each layer, that is, the 3 high-frequency features of wavelet transform Sub-band features, 6 directional modulus features of dual-tree complex wavelet transform, 4 high-frequency sub-band features of Contourlet transform, mean and variance features in 3×3 window of low-frequency sub-band of wavelet transform. Among them, the wavelet base used in the wavelet transform is the haar wavelet, the bases used in the dual-tree complex wavelet transform are near_sym_b and qshift_b, and the ‘9-7’ tower decomposition and directional filter bank are selected for the Contourlet transform.

[0024] Step 2: On each layer j, use the immune cloning algorithm to sele...

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Abstract

The invention discloses a texture image segmentation method based on an independent Gaussian hybrid model, which comprises the following segmentation steps: simultaneously performing three-layer wavelet transformation, dual-tree complex wavelet transformation and Contourlet transformation to training texture images; extracting the characteristics of the corresponding training texture images; selecting the characteristics by adopting an immunity clone algorithm on each layer; performing unsupervised learning of the Gaussian hybrid model to each layer of each training image, adaptively obtaining the corresponding component number, and thus obtaining the parameter of the Gaussian hybrid model; simultaneously performing wavelet transformation, dual-tree complex wavelet transformation and Contourlet transformation to test texture images; calculating the corresponding final likelihood value of each layer according to the transformation coefficient and the component number; obtaining the primary segmentation result through comparing the corresponding likelihood value of each texture; and obtaining the segmentation result through multi-scale fusion of the primary segmentation result. The invention has the characteristics of good consistence of segmentation area, complete information retaining, and accurate edge positioning, and can be used for the image texture recognition.

Description

technical field [0001] The invention belongs to the field of image processing, and relates to a texture image segmentation method, which can be used for image understanding and recognition. Background technique [0002] Texture image analysis and segmentation is one of the most classic research topics in image processing and computer vision. It plays an important role in national defense and national economy. It is used in image classification, image retrieval, image understanding, target recognition and other issues. played a key role. The purpose of texture segmentation is to divide an image into uniform regions and to determine the boundaries between regions. The regional consistency in the texture image is represented by the consistency of certain features of the texture in the region, and the segmentation must be performed on one or some features. Therefore, the extraction of texture features is a crucial factor affecting texture image segmentation. [0003] The curr...

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Application Information

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IPC IPC(8): G06T7/00G06K9/62
Inventor 侯彪邓倩倩刘凤焦李成王爽张向荣马文萍
Owner XIDIAN UNIV
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