Anomaly detection in fundus images by self-adaptive decomposition via local and color based sparse coding

Anomaly detection in color fundus images is challenging due to the diversity of anomalies. The current studies detect anomalies from fundus images by learning their background images, however, ignoring the affluent characteristics of anomalies. In this paper, we propose a simultaneous modeling strategy in both sequential sparsity and local and color saliency property of anomalies are utilized for the multi-perspective anomaly modeling. In the meanwhile, the Schatten p-norm based metric is employed to better learn the heterogeneous background images, from where the anomalies are better discerned. Experiments and comparisons demonstrate the outperforming and effectiveness of the proposed method.