Spectral clustering image segmentation method based on MOD dictionary learning sampling

Abstract

The invention discloses a spectral clustering image segmentation method based on MOD dictionary learning sampling, so that a problem that a segmentation result is not stable according to a current spectral clustering method is solved. The method is implemented by the following steps that: (1), feature extraction is carried out on a to-be-segmented image and extracted feature data are normalized to be between a range of [ 0, 1], so that a magnitude influence between data can be eliminated; (2), an MOD dictionary learning method is used to carry out learning on the normalized feature data so asto obtain a dictionary D; (3), Euclidean distances between the feature data and dictionary atoms are calculated as well as the first 1 data with a small distance are taken and utilized as a sampling subset S, wherein 1 takes a value of 300; (4), the Nystrom method is utilized to obtain feature vectors of all the feature data from the sampling subset S; and (5), k-means clustering is carried out on feature vectors corresponded to the first k feature values, so that a final image segmentation result is obtained. Compared with the prior art, the technology employed in the invention enables the provided method to have a stable image segmentation result with high accuracy; and the method can be applied to target detection and target identification.

Description

technical field [0001] The invention belongs to the technical field of image processing, relates to image segmentation, and can be used for target detection and target recognition on texture images and SAR images. Background technique [0002] Clustering is the process of distinguishing and classifying things according to certain requirements and rules. In this process, there is no prior knowledge about categories, and only the similarity between things is used as the criterion for category division, so it belongs to unsupervised categories of classification. Cluster analysis refers to the use of mathematical methods to study and process the classification of given objects. It is a kind of multivariate statistical analysis and an important branch of unsupervised pattern recognition. It divides a sample set without a class mark into several subsets according to certain criteria, so that similar samples are classified into one class as much as possible, and dissimilar samples...

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Problems solved by technology

However, since the k-means method itself is not a completely stable clustering method, when the sample space is not convex, the algorithm will fall into a local optimum, so the k-means Image segmentation results of spectral clustering method are very unstable

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