Department of Information Technology, Faculty of Computing & Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
Department of Computer Science & Engineering, Bangladesh University of Business & Technology, Dhaka 1216, Bangladesh.
Sensors (Basel). 2022 Mar 11;22(6):2188. doi: 10.3390/s22062188.
Image retrieval techniques are becoming famous due to the vast availability of multimedia data. The present image retrieval system performs excellently on labeled data. However, often, data labeling becomes costly and sometimes impossible. Therefore, self-supervised and unsupervised learning strategies are currently becoming illustrious. Most of the self/unsupervised strategies are sensitive to the number of classes and can not mix labeled data on availability. In this paper, we introduce AutoRet, a deep convolutional neural network (DCNN) based self-supervised image retrieval system. The system is trained on pairwise constraints. Therefore, it can work in self-supervision and can also be trained on a partially labeled dataset. The overall strategy includes a DCNN that extracts embeddings from multiple patches of images. Further, the embeddings are fused for quality information used for the image retrieval process. The method is benchmarked with three different datasets. From the overall benchmark, it is evident that the proposed method works better in a self-supervised manner. In addition, the evaluation exhibits the proposed method's performance to be highly convincing while a small portion of labeled data are mixed on availability.
由于多媒体数据的广泛可用性，图像检索技术变得越来越流行。目前的图像检索系统在标记数据上表现出色。然而，数据标记通常变得昂贵且有时不可能。因此，目前自我监督和无监督学习策略变得很有前途。大多数自我/无监督策略对类别的数量很敏感，并且不能在可用性上混合标记数据。在本文中，我们介绍了 AutoRet，这是一种基于深度卷积神经网络（DCNN）的自我监督图像检索系统。该系统基于成对约束进行训练。因此，它可以在自我监督下工作，也可以在部分标记数据集上进行训练。整体策略包括一个从多个图像块中提取嵌入的 DCNN。此外，嵌入被融合以获取用于图像检索过程的质量信息。该方法在三个不同的数据集上进行了基准测试。从整体基准测试中可以明显看出，该方法在自我监督方式下效果更好。此外，评估表明，当混合了一小部分可用的标记数据时，该方法的性能非常令人信服。
