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Image processing-based big data analysis method

A technology of image data and image processing, which is applied in the field of big data, can solve problems such as the difficulty of image processing database expansion, and achieve the effects of promoting development, short synchronization time, and expansion

Active Publication Date: 2018-09-18
浙江昕微电子科技有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] The purpose of the present invention is to provide a big data analysis method and system based on image processing in view of the difficulty in expanding the image processing database in the prior art

Method used

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  • Image processing-based big data analysis method

Examples

Experimental program
Comparison scheme
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Embodiment 1

[0038] This embodiment mainly provides a specific example of a big data analysis method based on image processing, including the following steps:

[0039] Classifying the first image data by the node to obtain the second image data;

[0040] The nodes mainly include virtual programs run by each user based on the same blockchain. Usually, such virtual programs can run on devices with computing power, such as a computer or a combination of a graphics card and a motherboard. The virtual program of the node can realize the The first image data is classified. The classification can be realized by directly storing the classification rules in the node, or storing the classifier in the node. The classifier is usually a trained general class classifier or a directional classifier. The choice of the specific classifier It is decided according to the final required sample data set. Of course, the classifier needs to be trained in advance for the node running program to use;

[0041] Tak...

Embodiment 2

[0051] On the basis of the method in the first embodiment above, the first image data (such as 1M, 250P) is the basic image information, and the basic image information includes such as image size and image resolution; wherein 1M represents the image size, and 250P represents the image resolution;

[0052] For example, if the node calls the car classifier program, the car classifier on the node will perform preliminary classification on the first image data (1M, 250P) to obtain the second image data. For example, the first image data will be trained by the classifier to obtain the first image The data belongs to the car, so the second image data (including the classification information of the first image data and the first image data, i.e. 1M, 250P, belonging to the car) is further obtained;

[0053] The node judges the legitimacy of the second image data again, and the legitimacy mainly refers to whether the second image data satisfies a preset condition, where the preset con...

Embodiment 3

[0056] On the basis of the method described in the second embodiment, a detailed description will be given for the classifier to be called by the node in the form of an API interface.

[0057] combine image 3 As shown, the compilation process of the classifier API is explained in detail:

[0058] (1) input neural network function, neural network function has defined neural network training parameter, and wherein neural network training parameter comprises (image size, image resolution, time stamp), certainly the present embodiment just illustrates, and the parameter of concrete selection is according to actual it depends;

[0059] (2) call the stored operation object function according to the neural network storage part, and the operation object function defines the operation object and combines the neural network parameters to construct the neural network model;

[0060] (3) Call the neural network compilation function (ANeuralNetworksCompilation_create()) to compile the n...

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Abstract

The invention discloses an image processing-based big data analysis method and system, and aims to solve the problem of high difficulty of image processing database expansion in the prior art. The method comprises the steps of classifying first image data by nodes to obtain second image data; judging the legality of the second image data by the nodes; sending the legal second image data by the nodes; receiving the legal second image data and performing writing in a block chain according to the legal second image data, wherein the block chain comprises a sample data set consisting of the secondimage data; and synchronizing the block chain by the nodes and obtaining the sample data set. According to the method, an image processing database is established by utilizing a block chain technology; the user nodes can participate in establishment of the image processing database; an image processing data set is expanded; node participants can synchronize the image processing data set in real time, so that the whole sample data set of image processing data is expanded; and the method is suitable for the field of big data.

Description

technical field [0001] The invention relates to the field of big data, in particular to a big data analysis method and system based on image processing. Background technique [0002] The main principle of the neural network is to obtain a training model through training a large number of samples, and to perform adaptive processing on other similar data samples through the training model. It has been widely used in various researches in recent years, such as computer vision, geological calculation, etc. In order to obtain a more accurate training model, more basic data samples are needed. The data of small classes may be subdivided in large class databases. If the data samples in small class databases are insufficient, the development of neural network training will inevitably be restricted. For example, obtain small passenger car samples and subdivide them into gray small passenger car samples in the small passenger car samples. If the small passenger car sample data set is...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30G06F9/50G06N3/04G06N3/08
CPCG06F9/5072G06N3/084G06N3/045Y02D10/00
Inventor 史玉成
Owner 浙江昕微电子科技有限公司
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