基于多源大数据分析的图像特征智能识别模型

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

针对图像受噪声影响导致特征识别精度下降的问题,提出了基于多源大数据分析的图像特征智能识别模型。根据改进的自适应二维中值滤波法,对所有小窗口内像素排查,过滤噪声,若检测到未被噪声污染的像素则直接输出,在单尺度模糊数学基础上,利用三高斯模型经过迭代处理均匀图像亮度值,将邻域中心点灰度取值分解为实部、虚部,提取局部二值特征和Brushlet特征,将邻近两个像素灰度值作减法运算,根据启发因子与灰度梯度最大值,智能识别图像特征。仿真实验结果表明:所提模型在图像不清楚情况下能均衡调节分辨率,且保证信息完整,识别结果精准有效,抗噪能力强。

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.