Semi-automatic image cutting method based on nuclear transfer

Abstract

The invention discloses a semi-automatic image cutting method based on nuclear transfer, which mainly solves the problems that the existing interactive image cutting method can not keep data consistency, the cutting result is not stable, and edges are not clear. The method is realized through the following steps: inputting an image to be cut, obtaining a superpixel set of the input image through a mean shift method, calculating a superpixel color column diagram feature set, building a similarity matrix W through a Bhattacharyya coefficient formula, performing interactive operation on the precut image by a user to obtain a seed superpixel set, building a must-link constrain set M and a cannot-link constrain set C, transferring constrain information M and C to the whole nuclear space through a nuclear transfer method, so as to obtain a nuclear matrix R, clustering the nuclear matrix R through a k mean clustering method, so as to obtain a clustering mark vector, and outputting and displaying a cutting result. The method has the advantages of good data consistency, stable cutting result, simplicity of operation and clear edges, and can be applied to image retrieval, the technology of converting 2D to 3D, target detection and tracking, and medical image analysis.

Description

technical field [0001] The invention belongs to the field of image processing, in particular to a semi-automatic image segmentation method based on kernel transfer, which can be used for target extraction and target detection. Background technique [0002] Image segmentation is one of the most basic problems in computer vision and digital image processing, and it is the basis for further analysis, recognition, tracking and understanding of images. Image segmentation divides the pixels in the image into different subsets according to different visual features or semantics. Specifically, image segmentation is the process of assigning a label to each pixel in the image, the purpose of which is to make pixels with similar visual features or the same semantics have the same label. At the same time, it is of great significance to study image segmentation, which can be widely used in many fields such as semi-automatic image retrieval, video conferencing, 2D to 3D technology, targe...

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Problems solved by technology

[0007] Although the above methods have improved the performance of image segmentation, there are still some problems: 1) the consistency between superpixel data points cannot be maintained; 2) the edge of the object of interest is not clear enough; 3) the characteristics of the image data itself have not been learned, so that These methods do not have global characteristics, and the segmentation results are unstable

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