Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (2): 555-561.doi: 10.13229/j.cnki.jdxbgxb20211312

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

Min FAN1(),Shi-jun SONG2()   

  1. 1.School of Civil Engineering，Southwest Jiaotong University，Chengdu 610031，China
    2.School of Transportation and Logistics，Southwest Jiaotong University，Chengdu 610031，China
  • Received:2021-12-01 Online:2023-02-01 Published:2023-02-28
  • Contact: Shi-jun SONG E-mail:fanmin2312@yeah.net;swjtulab@163.com

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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

Fig.1

Original multi-source image"

Fig.2

Comparison of preprocessing and extraction results"

Table 1

Comparison of information enhancement effects of three algorithms"

算法精度对比度信息熵所用时间/s
k-means88.2528.456.749.05
模糊数学78.9428.547.6612.34
本文94.5237.469.907.12
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