Research on image features extraction based on machine learning algorithms

Xiao-Chuang Chang
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Abstract

Image features are essential components for physical detection, classification of objectives and downstream tasks. Specifically, the image features can be utilized to automatically detect the characteristics of images and realize physical information mapping into information domains. However, existing image features are concentrated on the decrease the contrast, which can reduce the influence of lights. Another extraction process converts images into digital images and utilize digital information techniques to obtain the features. In this paper, we utilize the machine learning model to extract the features of images with enough training iterations. Initially, we utilize the CIFAR-10 data set, which contains the 10 categories of physical objectives and simulate as the training set. Indeed, the establish machine learning model is utilize to train through inputting the 80% of total data set. After training process, the output of machine learning mode can obtain the features of any physical images. Finally, we compare our proposed model with existing image features extraction methods and utilize 20% data to evaluate our model. From our extensive experimental results, we can conclude that our established model can effectively achieve the image features extraction with higher extraction accuracy and acceptable computation time through comparing with traditional mathematical analysis methods.
基于机器学习算法的图像特征提取研究
图像特征是物理检测、目标分类和下游任务的基本组成部分。具体来说,利用图像特征可以自动检测图像的特征,实现物理信息映射到信息域。然而,现有的图像特征集中在降低对比度上,可以减少光的影响。另一种提取方法是将图像转换为数字图像,并利用数字信息技术获得特征。在本文中,我们利用机器学习模型提取足够训练迭代的图像特征。首先,我们使用CIFAR-10数据集作为训练集,该数据集包含10类物理目标和模拟。实际上,通过输入总数据集的80%,利用建立的机器学习模型进行训练。经过训练过程,机器学习模式的输出可以获得任何物理图像的特征。最后,我们将我们提出的模型与现有的图像特征提取方法进行比较,并利用20%的数据对我们的模型进行评估。从我们广泛的实验结果来看,与传统的数学分析方法相比,我们所建立的模型可以有效地实现图像特征提取,提取精度更高,计算时间也可以接受。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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