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A brief introduction to traditional algorithms and deep learning algorithms for image classification
Image classification is a fundamental task in the field of computer vision that aims to predict an input image into one or more classes. This document will introduce some commonly used image classification algorithms in detail, including traditional methods and deep learning methods.
Traditional Image Classification Methods
Before the rise of deep learning techniques, researchers in the field of computer vision used traditional machine learning methods for image classification. These methods usually include two stages of feature extraction and classifier design. We will introduce three commonly used traditional image classification methods: k-nearest neighbor algorithm, support vector machine and random forest.
k-nearest neighbor algorithm (k-NN)
The k-nearest neighbor algorithm is a simple and intuitive classification method. Its basic idea is: given a new data point to be classified, find the k nearest neighbors in the training data set, and then vote according to the categories of these neighbors, and finally get the category of the new data point. The core of the k-NN algorithm is to calculate the distance between data points. Commonly used distance metrics include Euclidean distance, Manhattan distance, etc.
Support Vector Machine (SVM)
Support vector machine is a binary classification method that separates two types of data in a data set by finding a hyperplane. This hyperplane is called the maximum margin hyperplane because it tries to maximize the margin between the closest two classes of data points. For nonlinear separable problems, SVM can map data to a higher-dimensional space through a kernel function, making the data linearly separable. SVM has good generalization ability, but has high computational cost on large-scale datasets.
Random Forest (RF)
Random forest is an ensemble learning method that jointly classifies by building multiple decision trees. Each decision tree is trained using a randomly sampled subset with randomly selected features at each node. The classification result of random forest is the mode of all decision tree voting results. Random forest can effectively solve the problem of overfitting, and has good robustness and generalization ability.
Deep Learning Image Classification Method
With the development of deep learning techniques, convolutional neural network (CNN) has become the method of choice for image classification tasks. CNN has the ability to automatically extract features and classify, and can achieve excellent performance on large-scale data sets. Next, we introduce some representative CNN architectures.
Convolutional Neural Networks (CNNs)
Convolutional neural network is a special neural network structure, mainly including convolutional layer, activation layer, pooling layer and fully connected layer. The convolution layer is responsible for extracting the local features of the image, the activation layer introduces nonlinearity, the pooling layer reduces the feature dimension, and the fully connected layer realizes the final classification task.
LeNet-5
LeNet-5 is an early CNN architecture proposed by Yann LeCun in 1998. It consists of two convolutional layers, two pooling layers and three fully connected layers. The successful application of LeNet-5 to handwritten digit recognition tasks laid the foundation for the subsequent development of CNN.
AlexNet
In 2012, Alex Krizhevsky et al. proposed AlexNet, which is the first CNN structure to achieve significant performance improvement on large-scale image datasets. AlexNet includes 5 convolutional layers, 3 pooling layers and 3 fully connected layers. Compared with LeNet, AlexNet has a deeper network structure and larger parameter scale. In addition, it also introduces techniques such as ReLU activation function, Dropout technique and data augmentation.
VGG
In 2014, the Visual Geometry Group of Oxford University proposed the VGG network. The main feature of VGG is to use continuous 3x3 convolution kernels instead of larger convolution kernels, thereby reducing the number of parameters and improving computational efficiency. There are several versions of VGG, of which VGG-16 and VGG-19 are the most well-known.
Inception (GoogLeNet)
Also in 2014, the Google team proposed the Inception network (also known as GoogLeNet). The main innovation of Inception is the introduction of the Inception module, which is a structure that stacks convolution kernels of different scales in parallel. The Inception network greatly reduces the number of parameters while maintaining high performance.
ResNet
In 2015, Kaiming He et al. of Microsoft Research Institute proposed a residual network (ResNet). The key innovation of ResNet is the introduction of skip connection (skip connection), which enables the direct transfer of information between network layers. This structure effectively solves the problem of gradient disappearance in deep networks, allowing the network to reach a very large depth. ResNet achieved breakthrough results in the ImageNet competition with a depth of 152 layers.
DenseNet
In 2017, Gao Huang et al. proposed a densely connected network (DenseNet). The core idea of DenseNet is to connect the output of each layer to all subsequent layers to form a densely connected structure. This connection method can enhance feature propagation, improve the parameter utilization of the network, and reduce the training cost.
EfficientNet
In 2019, the Google team proposed EfficientNet, a CNN structure optimized based on neural network search technology (NAS). The main contribution of EfficientNet is to introduce a balanced network expansion strategy to improve performance by adjusting the depth, width and resolution of the network. EfficientNet achieves state-of-the-art performance on multiple image classification tasks while having low parameter volume and computational cost.
transfer learning
Transfer learning is a method of using pre-trained models for model training on new tasks. In image classification tasks, models pre-trained on large-scale datasets, such as ImageNet, are usually used. During training, the feature extraction part of the pre-trained model can be fixed, and only the newly added classifier part can be trained, or the parameters of the entire model can be fine-tuned. Transfer learning can reduce training time and data volume on new tasks while improving the generalization ability of the model.
data augmentation
Data augmentation is a technique that increases the amount of data by applying a series of random transformations to the original image. In image classification tasks, commonly used data augmentation methods include random cropping, random flipping, color jittering, rotation, etc. Data augmentation can effectively improve the robustness and generalization ability of the model, while reducing the overfitting of the model for specific samples in the data set.
Summarize
This paper introduces commonly used image classification algorithms, including traditional methods and deep learning methods. Traditional methods include k-nearest neighbor algorithm, support vector machine, and random forest; deep learning methods are mainly represented by convolutional neural networks, including LeNet-5, AlexNet, VGG, Inception, ResNet, DenseNet, and EfficientNet. Additionally, we introduce transfer learning and data augmentation, two techniques that can improve model performance. Overall, with the development of deep learning technology, more and more image classification tasks can be solved using deep learning methods. However, for small-scale datasets and resource-constrained situations, traditional methods still have certain advantages.