Image classification is a hot research topic in today's society and an important direction in the field of image processing research. SVM is a very powerful classification model in machine learning. CNN is a type of feedforward neural network that includes convolution calculation and has a deep structure. It is one of the representative algorithms of deep learning. Taking SVM and CNN as examples, this paper compares and analyzes the traditional machine learning and deep learning image classification algorithms. This study found that when using a large sample mnist dataset, the accuracy of SVM is 0.88 and the accuracy of CNN is 0.98; when using a small sample COREL1000 dataset, the accuracy of SVM is 0.86 and the accuracy of CNN is 0.83. The experimental results in this paper show that traditional machine learning has a better solution effect on small sample data sets, and deep learning framework has higher recognition accuracy on large sample data sets.

图像分类是当今社会研究的热点，也是图像处理研究领域的重要方向。SVM是机器学习中非常强大的分类模型。CNN是一种前馈神经网络，包括卷积计算并具有较深的结构。它是深度学习的代表性算法之一。以SVM和CNN为例，对传统的机器学习和深度学习图像分类算法进行比较和分析。研究发现，当使用大型样本mnist数据集时，SVM的精度为0.88，CNN的精度为0.98；当使用小的样本COREL1000数据集时，SVM的精度为0.86，而CNN的精度为0.83。