Image recognition and classification technology based on domain adaptation

Abstract With the explosive growth of image data, cross-domain image recognition and classification face huge challenges in many fields such as medical, security, and industry. For example, the differences in imaging equipment in different medical institutions lead to difficulties in medical image recognition, and the accuracy of cross-regional image recognition in security can drop by more than 30%. This paper focuses on cross-domain image recognition and classification technology based on transfer learning, adopts comparative research, selects typical public data sets such as Office-31, CUB-200-2011, and VLCS, and compares traditional manual features combined with SVM, DAN, DANN, MCD and other models with the innovative model of this paper. The experiment uses accuracy and F1 value as indicators. The results show that on the Office-31 dataset, the average accuracy of the proposed model is 0.845 and the F1 value is 0.805; on the CUB-200-2011 dataset, the average accuracy is 0.7725 and the F1 value is 0.7325; on the VLCS dataset, the average accuracy is 0.7925 and the F1 value is 0.7525, all of which are far higher than the comparison model. This shows that the proposed model can effectively improve the cross-domain image recognition and classification performance, and provide strong support for practical applications in multiple fields.