Image foreground and background segmentation method, image foreground and background segmentation network model training method, and image processing method and device

Abstract

The embodiment of the invention provides an image foreground and background segmentation network model training method, an image foreground and background segmentation method, an image processing method and device, and a terminal device. The image foreground and background segmentation network model training method comprises the following steps: obtaining the eigenvectors of a sample image to be trained; performing convolution on the eigenvectors to obtain the convolution results of the eigenvectors; magnifying the convolution results of the eigenvectors; determining whether the magnified convolution results of the eigenvectors satisfy the convergence conditions or not; if so, completing the training of the convolutional neural network model used for segmenting the foreground and background of the image; and if not, adjusting the parameters of the convolutional neural network model according to the convolution results of the amplified eigenvectors and performing iteration training on the convolutional neural network model according to adjusted parameters of the convolutional neural network model until the convolution results satisfy the convergence conditions. By means of the image foreground and background segmentation network model training method, the training efficiency of the convolutional neural network model is improved, and the training time is shortened.

Description

technical field [0001] Embodiments of the present invention relate to the field of artificial intelligence technology, and in particular, to a training method, device and terminal device for an image foreground and background segmentation network model, a method, device and terminal device for image foreground and background segmentation, and a video image processing method , devices and terminal equipment. Background technique [0002] Convolutional neural network is an important research field for computer vision and pattern recognition. It uses computers to imitate biological brain thinking to perform information processing similar to human beings on specific objects. With convolutional neural networks, object detection and recognition can be efficiently performed. With the development of Internet technology and the sharp increase in the amount of information, convolutional neural networks are more and more widely used in the field of object detection and recognition to ...

AI Technical Summary

Problems solved by technology

However, the current convolutional neural network training process is complicated, coupled with the increase in the number of training samples, resulting in long training time and high training costs

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