Image analysis method and system based on big data

Abstract

The invention relates to the technical field of image analysis, and discloses an image analysis method based on big data. The method comprises the following steps: obtaining massive images, and converting the images into grey-scale maps by using a grey-scale map conversion method; performing binarization processing on the gray-scale maps by using a local maximum between-cluster variance method toobtain binarized images; performing enhancement processing on the binarized images by using an image enhancement algorithm based on red channel compensation to obtain enhanced images; utilizing an image labeling algorithm based on deep learning to label image labels, and storing the images and the image labels into a distributed database; and caching the images and the image labels in the distributed database into the system by utilizing a path-based caching algorithm, thereby realizing more efficient image analysis and retrieval. The invention also provides an image analysis system based on big data. According to the invention, image analysis based on big data is realized.

Description

technical field [0001] The present invention relates to the technical field of image analysis, in particular to an image analysis method and system based on big data. Background technique [0002] With the advent of the era of big data, a large amount of image data is generated on the Internet every day. Traditional databases cannot store and process massive data sets, so using big data technology to store, analyze and process image data on the Internet has become a hot topic in the current research field. [0003] The images in special environments are relatively blurred, and it is difficult to analyze and process images, such as underwater images. The existing technology mainly starts from two aspects of chromaticity adjustment and contrast enhancement to repair underwater images, and has achieved certain results in some scenarios. , but it still seems to be stretched in terms of the effectiveness, robustness and real-time performance of the restoration algorithm. [000...

AI Technical Summary

Problems solved by technology

Traditional databases cannot store and process massive data sets, so using big data technology to store, analyze and process image data on the Internet has become a hot topic in the current research field

[0003] The images in special environments are relatively blurred, and it is difficult to analyze and process images, such as underwater images. The existing technology mainly starts from two aspects of chromaticity adjustment and contrast enhancement to repair underwater images, and has achieved certain results in some scenarios. , but it still seems stretched to take into account the effectiveness, robustness and real-time performance of the restoration algorithm

[0004] At the same time, the default traditional cache algorithm including LRU in the current mainstream data processing system cannot effectively meet the application characteristics and real-time requirements in the current environment, and it is easy to cause low hit rate, unnecessary I / O overhead and waste of resources. It is due to the fact that most of these caching algorithms do not make full use of the data-dependent semantic information in the data parallel system, but rely on traditional local information such as recent access information and frequency information of data items for cache management.

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