Research on Features Extraction and Classification for Images based on Transformer Learning

Image processing and analysis have become an essential method in many areas including medical impact, facial recognition, and social media analysis. With the rapid development of big data and artificial intelligence technology, especially the emergence of Transformer learning models, new methods have been brought to image feature extraction and classification. However, the existing transformer model limits the ability to handle variable-length sequences and understand complex sequence relationships. In this work, we propose a novel transformer-based framework that combines a self-attention mechanism and a multi-head attention technique to efficiently extract features from complex image data. In addition, we introduce an improved classifier that enables efficient image classification using extracted features. Our method takes into account not only the local features of the image but also the global relationships between different regions to achieve a more accurate representation of the features. We simulate our model with existing convolutional neural networks and other traditional machine learning methods in the public datasets including CIFAR-10 and MNIST. From our experimental results, we can observe that our transformer-learning-based framework shows significant performance improvement in image feature extraction and classifica-tion tasks.