Food Image Recognition Based on Anti-Noise Learning and Covariance Feature Enhancement

Food image recognition is a key research area in food computing, with widespread applications in dietary assessment, menu analysis, and nutrition monitoring. However, due to the influence of imaging devices and environmental factors, this technology is often subject to varying degrees of noise interference during application, which limits classification performance. To address this issue, we proposed a food image recognition method based on anti-noise learning and covariance feature enhancement. We designed a Noise Adaptive Recognition Module (NARM), which inputs additional noisy images during model training, treating denoising as an auxiliary task to progressively enhance the model's noise invariance and improve recognition accuracy. To mitigate the potential negative effects caused by the additional noise and to enhance the representation power of small eigenvalues, we innovatively proposed the Eigenvalue-Enhanced Global Covariance Pooling (EGCP) and integrated it into NARM. Simultaneously, we designed a new Weighted Multi-Granularity Fusion (WMF) method to improve feature extraction capability progressively. Additionally, by combining the Progressive Temperature-Aware Feature Distillation (PTAFD) method, we further optimized model efficiency without adding extra overhead to the original backbone network. The experimental outcomes show that the model we have developed delivers top-tier performance on the ETH Food-101 and Vireo Food-172 food image datasets. Specifically, our method achieves a Top-1 accuracy of 92.57 on ETH Food-101, surpassing other existing methods, and also performs well on the Top-5 accuracy on ETH Food-101 and the Top-1 and Top-5 accuracy on Vireo Food-172.