Yan Qiankun, Du Xiujuan, Li Chong, Tian Xiaojing
College of Computer, Qinghai Normal University, Xining, China.
Qinghai Provincial Key Laboratory of IoT, Xining, China.
Front Neurorobot. 2024 Jul 31;18:1423848. doi: 10.3389/fnbot.2024.1423848. eCollection 2024.
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.
针对现有方法在处理水下鱼类图像背景噪声抗干扰方面存在不足的问题，提出了一种用于水下鱼类图像分类的忽略背景的对比学习方法CLIB，以提高水下鱼类图像分类的准确性和鲁棒性。首先，CLIB通过提取模块有效地将图像中的主体与背景分离，并通过与原始图像组成三个互补视图将其应用于对比学习。为了进一步提高CLIB在复杂水下图像中的自适应能力，我们提出了一种基于多视图的对比损失函数，其核心思想是增强原始图像与主体之间的相似性，并最大化主体与背景之间的差异，使CLIB在训练过程中更专注于学习主体的核心特征，并有效忽略背景噪声的干扰。在Fish4Knowledge、Fish-gres、WildFish-30和QUTFish-89公共数据集上的实验表明，我们的方法表现良好，与基线模型相比，分别提高了1.43 - 6.75%、8.16 - 8.95%、13.1 - 14.82%和3.92 - 6.19%，进一步验证了CLIB的有效性。
