AdaptiveIntensityMix: Multi-Scale Complexity-Aware Image Data Augmentation Method

Data augmentation (DA) is a widely used technique to improve generalization in Deep Learning. However, many existing mixing-based approaches apply uniform strategies or rely on computationally expensive saliency guidance. In this work, we propose AdaptiveIntensityMix (AIM), a data augmentation method that adjusts mixing intensity based on local structural complexity. AIM combines edge magnitude and local variance at multiple scales to identify complex regions, applying conservative mixing to preserve informative structures and stronger mixing to simpler areas. Experiments on six datasets (CIFAR-10, CIFAR-100, Tiny ImageNet, Chest X-Ray, SVHN, and Food-50) show that AIM improves generalization over baseline and performs competitively with Mixup, CutMix, CutOut, and Random Erasing. Comparison with region-aware methods (SaliencyMix, PuzzleMix) shows that AIM achieves comparable accuracy with lower computational overhead. Statistical tests confirm that AIM performs better than baseline (p < 0.05). AIM provides an efficient augmentation strategy applicable across different visual domains.