A Multi-Scale Feature Extraction and Hierarchical Discriminant Analysis Approach for Image Recognition

Abstract

Traditional image recognition algorithms often face problems such as low recognition accuracy and insufficient robustness when facing complex scenes and multi-class image data. To this end, a hierarchical discriminant analysis (HDA)-based image recognition algorithm was proposed, which effectively improves image recognition performance by constructing a multi-scale feature extraction module, principal component analysis (PCA) dimensionality reduction, attention mechanism, and dynamic hierarchical adjustment strategy combined with a hierarchical feature extraction and discrimination model. The experiment was conducted on three public datasets: CIFAR-10, ImageNet subset (selecting 100 categories with a total of 150000 images, based on covering common object categories and moderate data volume for fair validation of algorithm performance), and MNIST. The performance was compared with models such as VGG16, ResNet50, SVM, KNN, Hierarchical CNN, EfficientNet, GoogLeNet, etc. The results indicated that the proposed method had higher recognition accuracy than other comparative algorithms on different datasets, with accuracies exceeding 90%. The proposed method performed better in terms of mean absolute error and root mean squared error. The F1 value curve of the proposed method was located at the top of the coordinate axis, reaching a maximum value of 92.39%, which was 14.56% higher than the lowest value of 78.24% in the EfficientNet model. This algorithm has better recognition accuracy than traditional algorithms on multiple public datasets, and has strong anti-interference ability and robustness, which can provide reference for optimizing the accuracy of image recognition.