Examples of practical applications of the combination of shallow features and deep features

Deep learning has achieved great success in the field of computer vision, and one of the important advances is the use of deep convolutional neural networks (CNN) for image classification. However, deep CNNs usually require large amounts of labeled data and computing resources. In order to reduce the demand for computational resources and labeled data, researchers began to study how to fuse shallow features and deep features to improve image classification performance. This fusion method can take advantage of the high computational efficiency of shallow features and the strong representation ability of deep features. By combining the two, computational costs and data labeling requirements can be reduced while maintaining high classification accuracy. This method is particularly important for application scenarios with small data volumes or limited computing resources. By in-depth study of the fusion method of shallow features and deep features, we can further improve the performance of image classification algorithms and bring more breakthroughs to research and applications in the field of computer vision.

A common method is to use a cascade CNN model. The first CNN model is used to extract shallow features, the second CNN model is used to extract deep features, and finally the The outputs of the two models are concatenated to improve the accuracy of the classification results.

This is an example of using a cascaded CNN model to recognize handwritten digits. The model uses the MNIST dataset, which includes 60,000 training images and 10,000 test images, each image size is 28×28 pixels.

First, we define the architecture of the model. We use two CNN models to extract features. The first CNN model contains two convolutional layers and a max pooling layer to extract shallow features. The second CNN model contains three convolutional layers and a max pooling layer to extract deep features. Next, we concatenate the outputs of the two models together and add two fully connected layers for classification. Such an architecture can extract rich features and perform better classification tasks.

import tensorflow as tf
from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, Flatten, Dense, Concatenate

# Define shallow CNN model
shallow_input = Input(shape=(28, 28, 1))
shallow_conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(shallow_input)
shallow_pool1 = MaxPooling2D((2, 2))(shallow_conv1)
shallow_conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(shallow_pool1)
shallow_pool2 = MaxPooling2D((2, 2))(shallow_conv2)
shallow_flat = Flatten()(shallow_pool2)
shallow_output = Dense(128, activation='relu')(shallow_flat)

# Define deep CNN model
deep_input = Input(shape=(28, 28, 1))
deep_conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(deep_input)
deep_pool1 = MaxPooling2D((2, 2))(deep_conv1)
deep_conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(deep_pool1)
deep_pool2 = MaxPooling2D((2, 2))(deep_conv2)
deep_conv3 = Conv2D(128, (3, 3), activation='relu', padding='same')(deep_pool2)
deep_pool3 = MaxPooling2D((2, 2))(deep_conv3)
deep_flat = Flatten()(deep_pool3)
deep_output = Dense(256, activation='relu')(deep_flat)

# Concatenate shallow and deep models
concatenate = Concatenate()([shallow_output, deep_output])
output = Dense(10, activation='softmax')(concatenate)

# Define the model
model = tf.keras.Model(inputs=[shallow_input, deep_input], outputs=output)

The model is then compiled and trained. Since the MNIST dataset is a multi-class classification problem, the cross-entropy loss function and Adam optimizer are used to compile the model. The model is trained on the training set for 100 epochs, using 128 batches for each epoch.

# Compile the model
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

# Train the model
model.fit([x_train, x_train], y_train, batch_size=128, epochs=100, verbose=1, validation_data=([x_test, x_test], y_test))

Finally, evaluate the model’s performance on the test set. In this example, the test accuracy of the cascaded CNN model is 99.2%, which is about 0.5% higher than the test accuracy trained with a single CNN model, indicating that the fusion of shallow features and deep features can indeed improve the performance of image classification.

In short, the fusion of shallow features and deep features is an effective method to improve the performance of image classification. This example shows how to use cascaded CNN models to recognize handwritten digits, where the first CNN model extracts shallow features, the second CNN model extracts deep features, and then the outputs of the two models are concatenated together for classification. This method is also widely used in many other image classification tasks.

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