Abstract
With the rapid development of computer vision and machine learning technology, the extraction and classification of image features has become one of the core tasks in these fields. Traditionally, classification tasks primarily relay on convolution neural networks, which are very effective at capturing local features of images. However, there are limitations when dealing with the global and complex relationships of images. In this work, we propose a novel transformer model to dispose the features extraction and classification task. Initially, the model explores how to apply the transformer model to the extraction of image features, including methods for segmentation and encoding of images. Additionally, the model can identify and understand the different parts and attributes of the image, which is essential for subsequent classification tasks. Subsequently, the model uses the extracted features to distinguish between different classes of images. The study demonstrates the classification performance of the model in a variety of complex scenarios, including those that require a detailed understanding of image content. From our extensive experiments analysis, we can conclude that our proposed model can achieve the features extraction and outperform classification accuracy with existing models.