Image prediction method based on multi-layer convolution long-short-term memory neural network

A long-short-term memory and neural network technology, applied in the field of image prediction based on multi-layer convolutional long-short-term memory neural network, can solve the problems of optical flow method with many steps, complicated work, and poor effect of memory neural network prediction details.

Active Publication Date: 2019-10-11
南京梅花软件系统股份有限公司
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Problems solved by technology

[0006] The purpose of the present invention is to address the above-mentioned problems, in order to enhance the ability to express the details of the image structure of the network, propose an image prediction method based on a multi-layer convolutional long-short-term memory neural networ

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  • Image prediction method based on multi-layer convolution long-short-term memory neural network

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[0052] Preferred embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although preferred embodiments of the invention are shown in the drawings, it should be understood that the invention may be embodied in various forms and should not be limited to the embodiments set forth herein.

[0053] The image prediction method of the multi-layer convolution long-short-term memory neural network of the present embodiment comprises the following steps:

[0054] Step 1) Acquisition of training data: In this embodiment, the precipitation radar echo data in the Alibaba Tianchi Big Data Competition is selected, and the data mainly reflects the radar echo situation in a few hours a day in the rainstorm season. There are 61 radar echo images in the original data set, one image is taken every 6 minutes, m sequence images are selected as the training data set by sampling at fixed intervals, m is an even number, and the followin...

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Abstract

The invention relates to an image prediction method based on a multi-layer convolution long-short-term memory neural network, and the method comprises the steps: extracting the change features of an inputted sequence image through a multi-layer convolution long-short-term memory neural network module, and extracting more abstract features through the deepening of single-layer convolution, therebyimproving the expression capability. In the prediction process, the extracted change features are utilized to predict a next image. The predicted image is input back to the prediction module again, sothat a next image is obtained. A single-layer convolution long-short-term memory neural network of an original method is poor in capability of extracting change features of sequence images, and showsthat structural details of the images output during prediction are fuzzy. According to the method, by deepening the convolution layer, more accurate change features are extracted, and the problem ofstructural detail blurring of the predicted image is obviously improved.

Description

technical field [0001] The invention belongs to the technical field of computer vision, and in particular relates to an image prediction method based on a multi-layer convolutional long-short-term memory neural network. Background technique [0002] With the rapid development of deep learning in recent years, more and more computer vision problems have been well solved. In addition, with the development of the economy and the improvement of living standards, people need more accurate image prediction. For example: precipitation data, human health prediction data, etc. [0003] For agricultural products and flowers cultivated with new technologies around cities, some agricultural product processing factories that need to process products in the open air; and airports, etc.; all require more harsh weather conditions. The present invention models this problem using a convolutional long short-term memory neural network in deep learning. [0004] For human health, it is also n...

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Application Information

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IPC IPC(8): G06N3/04G06N3/08G06Q10/04
CPCG06N3/049G06N3/084G06Q10/04G06N3/045G06N3/048Y02A90/10
Inventor 强星乐卫清潘卫东花月明
Owner 南京梅花软件系统股份有限公司
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