基于多源大数据分析的图像特征智能识别模型

doi:10.13229/j.cnki.jdxbgxb20211312

Abstract

Abstract:

Due to the influence of image noise， the accuracy of image feature recognition is reduced. Therefore， an intelligent image feature recognition model based on multi-source big data analysis is proposed. According to the improved adaptive two-dimensional median filtering method， all pixels in the small window are checked and the noise is filtered. If the pixels that are not polluted by noise are detected， they are directly output. On the basis of single scale fuzzy mathematics， the three Gaussian model is used to iteratively process the uniform image brightness value， The gray value of the neighborhood center point is decomposed into the real part and the imaginary part， the local binary feature and the Brushlet feature are extracted， the gray value of the adjacent two pixels is subtracted， and the image features are intelligently recognized according to the heuristic factor and the maximum gray gradient. The simulation results show that the proposed model can adjust the resolution evenly when the image is unclear， and ensure the integrity of information， accurate and effective recognition results， and strong anti noise ability.

Key words: median filtering, three Gaussian model, surround function, local binary mode characteristics, brushlet domain complex feature, set domain adaptive fast algorithm

CLC Number:

TP391

Min FAN,Shi-jun SONG. Intelligent recognition model of image features based on multi⁃source big data analysis[J].Journal of Jilin University(Engineering and Technology Edition), 2023, 53(2): 555-561.

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References 20

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算法	精度	对比度	信息熵	所用时间/s
k-means	88.25	28.45	6.74	9.05
模糊数学	78.94	28.54	7.66	12.34
本文	94.52	37.46	9.90	7.12

Intelligent recognition model of image features based on multi⁃source big data analysis

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