Research on Image Feature Extraction and Environment Inference Based on Invariant Learning

As dataset environments evolve, the adaptability of deep models has weakened due to biases in training data collection. Consequently, a critical challenge has emerged: enabling models to effectively learn invariant features across diverse environments while ignoring spurious features introduced by environmental changes. This article proposes an image feature extraction algorithm based on invariant learning, which trains a ResNet18 model that can fully learn invariant features. On the basis of this model, GRAD-CAM algorithm is used to extract environmental features of images. Based on this feature dataset, images are classified according to different environments through K-means clustering, achieving environmental partitioning of mixed datasets. The results show that on the test set, the IRM-ResNet18 network’s prediction accuracy is 88.6%, and its accuracy and stability are significantly better than those of ResNet18. It can fully learn and extract invariant features from images. By segmenting the image based on the extracted environmental features, The findings indicate that the IRM-ResNet18 network’s total environmental segmentation accuracy is 88.2%, which confirms the efficacy of the image environmental segmentation algorithm proposed in this paper.