Learning method for zero-based image recognition

Image recognition based on zero-shot learning is an emerging technology, which is different from traditional image recognition methods. Traditional image recognition requires learning features and classification rules through training data, while zero-shot learning does not require pre-training the model. It performs real-time classification based on the characteristics of the image to be recognized, thereby achieving fast and accurate recognition. Image recognition with zero-shot learning has been widely used in smart home, face recognition, smart security and other fields. It can help smart home devices quickly identify user needs and respond accordingly. In face recognition, zero-shot learning can accurately identify faces based on their features and improve recognition accuracy. In the field of intelligent security, zero-shot learning can help identify dangerous objects and provide a safer and more reliable monitoring system. In short, image recognition technology based on zero-shot learning is fast and accurate, providing more intelligent solutions for various fields.

Zero-shot image recognition is mainly divided into two stages: feature extraction and classification.

In the feature extraction stage, the zero-shot learning image recognition algorithm will automatically analyze various features in the image to be recognized, such as color, shape, texture, etc., and represent them as vectors . These vectors can be regarded as the "fingerprints" of the image to be recognized and used for subsequent classification.

In the classification stage, the zero-shot learning image recognition algorithm uses feature vectors to compare with previously learned category feature vectors to find the category closest to the image to be recognized. These category feature vectors are extracted from other images, and they represent features of each category. When recognizing a new image, the zero-shot learning image recognition algorithm assigns the image to be recognized to the closest category based on how similar it is to the feature vectors of each category.

In order to better understand zero-shot learning, we can illustrate it through an example. We adopt the Animals with Attributes 2 (AWA2) dataset, which contains 50 different animal categories, each category is described by 85 attributes. We randomly selected 10 categories as the training set and the remaining 40 categories as the testing set. We used an attribute-based approach for model training.

First, we need to import the necessary libraries and datasets:

import numpy as np
import pandas as pd
import scipy.io as sio
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression

# 导入数据集
data = sio.loadmat('data/awa2.mat')
train_labels = data['train_labels'].astype(int).squeeze()
test_labels = data['test_labels'].astype(int).squeeze()
train_attributes = StandardScaler().fit_transform(data['train_attributes'])
test_attributes = StandardScaler().fit_transform(data['test_attributes'])

Then, we need to convert the attribute descriptions into vectors in the embedding space. We use principal component analysis (PCA) to convert attribute descriptions into vectors in embedding space. We select the first 10 principal components as embedding vectors.

from sklearn.decomposition import PCA

# 将属性描述转换为嵌入空间中的向量
pca = PCA(n_components=10)
train_embed = pca.fit_transform(train_attributes)
test_embed = pca.transform(test_attributes)

Next, we need to train a classifier to predict the categories in the test set. We use logistic regression as classifier.

# 训练分类器
clf = LogisticRegression(random_state=0, max_iter=1000)
clf.fit(train_embed, train_labels)

# 在测试集上进行预测
predicted_labels = clf.predict(test_embed)

Finally, we can calculate the accuracy to evaluate the performance of the model.

# 计算准确率
accuracy = np.mean(predicted_labels == test_labels)
print('Accuracy:', accuracy)

In this example, we used an attribute-based approach to train the model and selected the first 10 principal components as embedding vectors. Finally, we got a model with an accuracy of 0.55 on the test set.

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