基于深度学习的图像分类算法研究与应用
摘 要
本文着重介绍深度学习算法在图像分类中的研究和应用。首先，介绍了深度学习算法中神经网络模型、激活函数、损失函数和优化算法等基础知识。然后，对图像分类问题进行了定义和基本流程的讲解，并介绍了特征提取方法和表示方法以及常见的图像分类算法和评价方法。接着，重点介绍了卷积神经网络（CNN）的基本结构和思想，探讨了深度卷积神经网络的改进方法和应用，以及迁移学习方法在图像分类中的应用。最后，得出结论：深度学习算法在图像分类问题中有着广泛的应用前景，未来还有很大的研究和发展空间。
关键词：深度学习算法、图像分类、卷积神经网络、特征提取、迁移学习
Abstract
This article focuses on the research and application of deep learning algorithms in image classification. Firstly, the basic knowledge of deep learning algorithm, such as neural network model, activation function, loss function and optimization algorithm, is introduced. Then, the definition and basic process of image classification were explained, and feature extraction and representation methods, as well as common image classification algorithms and evaluation methods were introduced. Then, the basic structure and idea of convolutional neural network (CNN) are introduced, and the improved methods and applications of deep convolutional neural network are discussed, as well as the application of transfer learning in image classification. Finally, it is concluded that deep learning algorithms have broad application prospects in image classification problems, and there is still great research and development space in the future.
Keyword: Deep learning algorithm、image classification、convolution neural network、feature extraction、transfer learning
目 录
1引言 1
2深度学习算法介绍 2
2.1神经网络模型 2
2.2激活函数 3
2.3损失函数 4
2.4优化算法 5
3图像分类问题 6
3.1图像分类问题的定义和基本流程 6
3.2特征提取方法和特征表示方法 7
3.3常见的图像分类算法和评价方法 8
4深度学习在图像分类中的应用 9
4.1卷积神经网络（CNN）的基本结构和思想 9
4.2深度卷积神经网络的改进方法和应用 10
4.3迁移学习方法在图像分类中的应用 11
5结语 12
参考文献 13
致谢 14
摘 要
本文着重介绍深度学习算法在图像分类中的研究和应用。首先，介绍了深度学习算法中神经网络模型、激活函数、损失函数和优化算法等基础知识。然后，对图像分类问题进行了定义和基本流程的讲解，并介绍了特征提取方法和表示方法以及常见的图像分类算法和评价方法。接着，重点介绍了卷积神经网络（CNN）的基本结构和思想，探讨了深度卷积神经网络的改进方法和应用，以及迁移学习方法在图像分类中的应用。最后，得出结论：深度学习算法在图像分类问题中有着广泛的应用前景，未来还有很大的研究和发展空间。
关键词：深度学习算法、图像分类、卷积神经网络、特征提取、迁移学习
Abstract
This article focuses on the research and application of deep learning algorithms in image classification. Firstly, the basic knowledge of deep learning algorithm, such as neural network model, activation function, loss function and optimization algorithm, is introduced. Then, the definition and basic process of image classification were explained, and feature extraction and representation methods, as well as common image classification algorithms and evaluation methods were introduced. Then, the basic structure and idea of convolutional neural network (CNN) are introduced, and the improved methods and applications of deep convolutional neural network are discussed, as well as the application of transfer learning in image classification. Finally, it is concluded that deep learning algorithms have broad application prospects in image classification problems, and there is still great research and development space in the future.
Keyword: Deep learning algorithm、image classification、convolution neural network、feature extraction、transfer learning
目 录
1引言 1
2深度学习算法介绍 2
2.1神经网络模型 2
2.2激活函数 3
2.3损失函数 4
2.4优化算法 5
3图像分类问题 6
3.1图像分类问题的定义和基本流程 6
3.2特征提取方法和特征表示方法 7
3.3常见的图像分类算法和评价方法 8
4深度学习在图像分类中的应用 9
4.1卷积神经网络（CNN）的基本结构和思想 9
4.2深度卷积神经网络的改进方法和应用 10
4.3迁移学习方法在图像分类中的应用 11
5结语 12
参考文献 13
致谢 14
