Small sample image segmentation method based on guide network and full-connection conditional random field

Abstract

The invention discloses a small sample image segmentation method based on a guide network and a full-connection conditional random field, and the method comprises the steps: carrying out the group division of an obtained to-be-segmented image, and obtaining a support image and a query image; marking positive sample points and negative sample points in the support image to obtain a foreground information feature map and a background information feature map containing positive and negative sample positions; based on the support image, the foreground information feature map and the background information feature map, extracting task features by adopting a guide network; performing preliminary segmentation according to the task features and the query image to obtain a preliminary segmentation result; carrying out edge refinement on the preliminary segmentation result based on a full-connection conditional random field to obtain a final segmentation result; inferring potential features of the support image by optimizing the guide network; performing preliminary segmentation on the query image without pixel annotation according to the potential features; and according to the preliminary segmentation result, carrying out finer segmentation through a full-connection conditional random field so as to obtain a relatively high segmentation result.

Description

technical field [0001] The invention relates to the technical field of image processing, and relates to a small-sample image segmentation method, in particular to a small-sample image segmentation method based on a guided network and a fully connected conditional random field. Background technique [0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art. [0003] Image segmentation technology is the core task and important research direction in the field of computer vision. It refers to the process of dividing an image into several regions with similar properties. From a mathematical point of view, it is the process of dividing an image into mutually disjoint regions. In recent years, with the in-depth development of deep learning technology, image segmentation technology has achieved good results, especially the emergence of convolutional neural networks, and many segmentation ...

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Problems solved by technology

[0006] Aiming at the deficiencies in the prior art and solving the problems of how to improve the large amount of training data and the low segmentation accuracy in the prior art, the present invention proposes a small-sample image segmentation method based on a guided network and a fully connected conditional random field. Infer the latent features of the supporting image by optimizing the guided network; perform preliminary segmentation on the query image without pixel annotations according to the latent features; perform more refined segmentation through the fully connected conditional random field according to the preliminary segmentation results, so as to obtain relatively high segmentation results

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