基于深度学习的图像分类方法研究

Research on image classification based on deep learning

随着信息时代的发展,图像数据的数量不断增加。在海量的图像数据中提取有效信息,需要进行图像分类检索,而图像分类方法的选择与应用对图像分类的准确性和效率有重要影响。图像分类的主要流程包括获取图像、预处理、特征提取和识别分类。传统的图像分类方法效率较慢且准确性较低,而基于深度学习的图像分类方法可以利用复杂的机器学习方法提高图像分类的速度和准确性,具有良好的应用价值。

With the development of the information age,the quantity of image data is constantly increasing.Extracting effective information from massive image data requires image classification and retrieval,and the selection and application of image classification methods play an important role in the accuracy and efficiency of image classification.The main process of image classification includes image acquisition,preprocessing,feature extraction,and recognition classification.Traditional image classification methods have slower efficiency and lower accuracy,while deep learning based image classification methods can utilize complex machine learning methods to improve the speed and accuracy of image classification,thus having good application value.