CLIB: Contrastive learning of ignoring background for underwater fish image classification

Abstract

Aiming at the problem that the existing methods are insufficient in dealing with the background noise anti-interference of underwater fish images, a contrastive learning method of ignoring background called CLIB for underwater fish image classification is proposed to improve the accuracy and robustness of underwater fish image classification. First, CLIB effectively separates the subject from the background in the image through the extraction module and applies it to contrastive learning by composing three complementary views with the original image. To further improve the adaptive ability of CLIB in complex underwater images, we propose a multi-view-based contrastive loss function, whose core idea is to enhance the similarity between the original image and the subject and maximize the difference between the subject and the background, making CLIB focus more on learning the core features of the subject during the training process, and effectively ignoring the interference of background noise. Experiments on the Fish4Knowledge, Fish-gres, WildFish-30, and QUTFish-89 public datasets show that our method performs well, with improvements of 1.43–6.75%, 8.16–8.95%, 13.1–14.82%, and 3.92–6.19%, respectively, compared with the baseline model, further validating the effectiveness of CLIB.