Image segmentation method combined with semi-supervised learning

Abstract

The invention discloses an image segmentation method combined with semi-supervised learning, and the method comprises the following steps: obtaining a gradient image of a to-be-segmented image, sequentially carrying out the compression, sharpening, binarization processing and distance transformation of the to-be-segmented image, and obtaining a distance topographic map of the to-be-segmented image; extracting one point or point set with the biggest gray value in each communication region of an obtained distance transformation image, and enabling the point or point set to serve as a foreground mark; carrying out the watershed transformation of the obtained distance topographic map, and enabling an obtained watershed ridge line to serve as a background mark; shielding a local minimum value in the gradient image, marking the local minimum value of the gradient image according to the obtained foreground mark and background mark, and obtaining the corrected gradient image; and carrying out the obtaining of multi-angle data, the building of a prediction matrix, the building of a training model and the segmentation of an image through a semi-supervised learning method. The image segmentation method can improve the precision of image segmentation.

Description

technical field [0001] The invention relates to a graphic segmentation method, in particular to an image segmentation method based on the combination of semi-supervised learning. Background technique [0002] Image segmentation and object extraction, as an important branch in the field of image processing and computer vision, has always attracted the attention of many researchers. At the same time, image segmentation and target extraction are also widely used in pattern recognition, computer vision, artificial intelligence and other fields. Therefore, in-depth research on image segmentation and target extraction is not only helpful to the perfect solution of image segmentation and target extraction, but also helps to 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 significance in the fie...

AI Technical Summary

Problems solved by technology

[0004] Traditional image segmentation methods include: mean shift method, normalized segmentation method and K-means method, etc., generally have low segmentation accuracy, cannot segment objects with no obvious boundaries in the cohesive area, and are easily confused with the image background, resulting in under-segmentation