Research on computer image recognition and classification algorithms for complex scenes

Image recognition and classification algorithms are more and more widely used in various complex scenes. However, the problems of diverse visual features, occlusion phenomenon, illumination variation and background interference in complex scenes bring great challenges to the existing algorithms. To address the challenges of image recognition and classification in complex scenarios, this paper thoroughly investigates existing mainstream image processing algorithms and introduces an enhanced model that leverages deep learning and multi-scale feature extraction. By integrating convolutional neural network (CNN) and attention mechanism, the model realizes efficient extraction of detailed features in images and global perception of scene information. Experimental results show that the proposed algorithm is superior to traditional algorithms in image recognition accuracy and classification performance in complex scenes, and has high robustness and generalization ability, providing effective technical support for applications in related fields.