Method for segmenting images by utilizing sparse representation and dictionary learning

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...

AI Technical Summary

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 this method is not applicable when sample labels are not available

[0004] The K-means algorithm does not require known samples, and it can be segmented directly based on unknown data characteristics, but the disadvantage is that the selection of the initial point is random, so the result is not stable

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