Advanced Feature Extraction Algorithms for Deep Learning in Image Recognition

Image recognition is an important research direction in the field of modern computer vision (CV), and extracting image features is its core step, and its efficiency directly determines the speed and accuracy of the entire image recognition. In recent years, deep learning (DL) technology has made significant breakthroughs in the field of CV, especially in image classification tasks, thanks to its powerful feature representation ability. Among them, Convolutional Neural Networks (CNN), as an important branch of DL, have achieved commendable results in image recognition and classification tasks with their unique convolution and pooling operations. This article proposes a CNN algorithm based on the combination of attention mechanism (Attention-CNN), aiming to improve the efficiency and accuracy of image feature extraction. The attention mechanism can guide the network to pay more attention to key regions in the image during the feature extraction process, thereby extracting more representative and robust advanced features. The experimental results show that compared with traditional CNN algorithms, our algorithm has improved image recognition accuracy and feature extraction efficiency.