Graph theory and semi supervised learning combination-based image segmentation method

A semi-supervised learning and image segmentation technology, which is applied in image analysis, image data processing, instruments, etc., can solve the problems of not considering the characteristics of multiple perspectives of the image and low segmentation accuracy, and achieve the effect of improving image segmentation quality and accuracy

Inactive Publication Date: 2016-05-25
SHAANXI NORMAL UNIV
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Problems solved by technology

[0005] Traditional image segmentation methods include: mean shift method, normalized segmentation method, and K-means method, etc., which generally have defects such as low segmentation accuracy and failure to consider the characteristics of multiple viewing angles of the image.

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Embodiment Construction

[0030] In order to make the objectives and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention, but not to limit the present invention.

[0031] The embodiment of the present invention provides an image segmentation method based on the combination of graph theory and semi-supervised learning, which includes the following steps:

[0032] S1, input N in the image database 1 Training images and preprocessing them to obtain the local feature matrix of each image in N1 training images; the image database contains N images of size m×n that have been manually classified and labeled, N 1 <N;

[0033] S2, calculate the local feature matrix I i Select any kernel function for the mean value of the covariance matrix, and map the mean value of the covariance matrix to the kernel subspace of t...

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Abstract

The invention discloses a graph theory and semi supervised learning combination-based image segmentation method. According to the method, data are mapped to a kernel space by using a kernel principal component analysis method; an image is binarized, and then, the binarized image is segmented into a certain number of regional blocks; each image region obtained after the segmentation is adopted as one node; and the acquisition of multi-angle data, the establishment of a prediction matrix, the construction of a training model and the segmentation of the image are carried out through a semi supervised learning method. With the method of the invention adopted, the accuracy of image segmentation can be improved, the promotion of the development of pattern recognition, computer vision, artificial intelligence and the like can be facilitated.

Description

Technical field [0001] The invention relates to a graph segmentation method, in particular to an image segmentation method based on the combination of graph theory and semi-supervised learning. Background technique [0002] Image segmentation and target extraction, as an important branch in the field of image processing and computer vision, have always attracted the attention of many researchers. At the same time, image segmentation and target extraction also have a wide range of applications in pattern recognition, computer vision, artificial intelligence and other fields. Therefore, the in-depth research on image segmentation and target extraction will not only help the perfect solution of image segmentation and target extraction, but also help promote the development of pattern recognition, computer vision, artificial intelligence and other fields. [0003] At present, image segmentation is mainly used to realize the classification of unknown categories of data. It is of great ...

Claims

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Application Information

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IPC IPC(8): G06T7/00
Inventor 马君亮肖冰汪西莉何聚厚
Owner SHAANXI NORMAL UNIV
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