Method for performing image segmentation by using manifold spectral clustering

Abstract

The invention disclose a method for performing image segmentation by using manifold spectral clustering, which is used for solving the problems of large storage capacity and low computing efficiency and segmentation accuracy in the existing method. The method for performing the image segmentation by using the manifold spectral clustering comprises the following steps: (1) inputting an image, extracting colors and textural features of the image, and obtaining a manifold set of the input image by using a watershed algorithm; (2) computing the manifold feature set, constructing a distance matrix, and acquiring a manifold distance matrix by using the Floyd algorithm; (3) computing a similarity matrix so as to construct a degree matrix and a normalization laplacian matrix; (4) carrying out eigen-decomposition on the normalization laplacian matrix so as to construct a spectral matrix; and (5) normalizing the spectral matrix to obtain a normalization spectral matrix, acquiring the label vector of the manifold set by a K-means algorithm, and outputting a segmentation result. The method for performing the image segmentation by using the manifold spectral clustering has the advantages of small storage capacity and high computing efficiency and segmentation accuracy, and can be used for detecting focal areas of medical images, detecting defects on precision component surfaces, and processing geographic and geomorphic pictures shot by satellites.

Description

technical field [0001] The invention belongs to the field of image processing, and relates to an image segmentation method, in particular to an image segmentation method based on spectral clustering, which can be used for target detection and tracking and processing of terrain and landform photos taken by satellites. Background technique [0002] Digital image processing technology is an interdisciplinary field. With the continuous development of computer science and technology, image processing and analysis has gradually formed its own scientific system. Image segmentation is an important image processing technology that can be applied to medical image detection of lesion areas, surface defect detection of precision parts, and processing of satellite images. topographical photos, etc. Image segmentation is a key step from image processing to image analysis. It can be said that the quality of image segmentation results directly affects the understanding of images. The tech...

AI Technical Summary

Problems solved by technology

Although this method overcomes the problem of large amount of data, there are still some problems: 1) random selection of sample points makes its results unstable; 2) it still uses the Gaussian similarity function to calculate the similarity, and the scale parameters must be manually set; 3 ) It takes a long time to calculate the similarity between the sample point and the remaining pixels in the image, making the segmentation efficiency low

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