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

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 ...

AI Technical Summary

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.