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.
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