Method for segmenting images by utilizing sparse representation and dictionary learning

An image segmentation and sparse representation technology, applied in the field of image processing, can solve the problems of unstable results, inapplicability, accuracy dependence, etc., to achieve accurate segmentation results, wide application of algorithms, and stable image segmentation effects.

Active Publication Date: 2011-06-15
XIDIAN UNIV
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Problems solved by technology

The disadvantage of this method is that the correct rate is more dependent on the selection of the base. When a better sample is selected as the base, the method can achieve a better segmentation effect, and the sparse representation classifier is a supervised method. Sample labels are required, so

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  • Method for segmenting images by utilizing sparse representation and dictionary learning
  • Method for segmenting images by utilizing sparse representation and dictionary learning
  • Method for segmenting images by utilizing sparse representation and dictionary learning

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Embodiment Construction

[0023] Refer to attached figure 1 , concrete steps of the present invention include:

[0024] Step 1. Input the image to be segmented with a size of N×N, and extract the feature matrix M of the image to be segmented

[0025] The texture features extracted from the image to be segmented in the present invention include grayscale co-occurrence features and wavelet features:

[0026] 1a) Use the gray level co-occurrence matrix to extract features from the image to be segmented

[0027] Generate a gray level co-occurrence matrix p for the image to be segmented ij (s, θ), where s is the pixel point x i and x j The distance between, the value of θ is 4 discrete directions: 0°, 45°, 90°, 135°, and three statistics are taken in each direction: second-order moment of angle, homogeneous area, contrast, Each statistic is calculated according to the following formula:

[0028] Angular second moment: f 1 = Σ i ...

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Abstract

The invention discloses a method for segmenting images by sparse representation and dictionary learning, and the method is mainly used for solving the problem of unstable division result under the condition of no sample label in the prior art. The method comprises the following steps: (1) inputting an image to be segmented, and extracting the gray co-occurrence features and wavelet features of the image to be segmented; (2) carrying out K-means clustering on the image to be segmented by utilizing the features so as to obtain K-feature points; (3) acquiring K dictionaries corresponding to the K-feature points by an KSVD (K-clustering with singular value decomposition) method; (4) carrying out sparse decomposition on all the features of the K dictionaries by a BP (back propagation) algorithm to obtain a sparse coefficient matrix; (5) calculating the sparse representation error of each dictionary according to each feature point, and dividing the point corresponding to the feature to the type with the smallest dictionary error; and (6) repeating the step (5) until all the points have label values, and finishing final segmentation. Compared with the prior art, the method can be used for significantly improving the image stability and the segmentation performance, and can be used for target detection and background separation.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to an image segmentation method, which can be used to segment texture images. Background technique [0002] Image segmentation is one of the basic and key technologies in image processing and computer vision, and its purpose is to separate the target from the background. Image segmentation refers to the technology and process of dividing the image into regions with different characteristics and extracting the target of interest, which provides the basis for subsequent classification, identification and retrieval. Here, the feature can be the grayscale, color, texture, etc. of the pixel, and the pre-corresponding target can correspond to a single area or multiple areas. Image segmentation is widely used in almost all fields of image processing, such as medical image analysis, military research, remote sensing meteorology, traffic image analysis, etc. Image segmentation method...

Claims

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Application Information

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IPC IPC(8): G06K9/46G06K9/62
Inventor 杨淑媛焦李成朱君林韩月胡在林王爽侯彪刘芳缑水平
Owner XIDIAN UNIV
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