Generally, automatic image annotation can offer semantic graphics for recognizing image contents, and it creates a base for devising various techniques, which can search images in a huge dataset. Although most existing techniques mainly focus on resolving annotation issues through sculpting tag semantic information and visual image content, it ignores additional information, like picture positions and descriptions. The established Exponential Sailfish Optimizer-based Generative Adversarial Networks are therefore used to provide an efficient approach for image annotation (ESFO-based GAN). By combining Exponentially Weighted Moving Average (EWMA) and Sailfish Optimizer (SFO), the ESFO is a newly created design that is used to train the GAN classifier. Additionally, the Grabcut is presented to successfully do image annotation by extracting the background and foreground images. Additionally, DeepJoint segmentation is used to divide apart the images based on the background image that was extracted. Finally, image annotation is successfully accomplished with the aid of GAN. As a result, image annotation uses the produced ESFO-based GAN's subsequent results. The developed approach exhibited enhanced outcomes with maximum F-Measure of 98.37%, maximum precision of 97.02%, and maximal recall of 96.64%, respectively, using the flicker dataset.

通常，自动图像注释可以提供用于识别图像内容的语义图形，并为设计各种技术奠定基础，这些技术可以在庞大的数据集中搜索图像。尽管大多数现有技术主要侧重于通过雕刻标签语义信息和视觉图像内容来解决注释问题，但它忽略了附加信息，如图片位置和描述。因此，已建立的基于指数旗鱼优化器的生成对抗网络被用于提供一种有效的图像注释方法（基于 ESFO 的 GAN）。通过结合指数加权移动平均线 (EWMA) 和旗鱼优化器 (SFO)，ESFO 是一种新创建的设计，用于训练 GAN 分类器。此外，提出了 Grabcut，通过提取背景和前景图像成功地进行图像标注。此外，DeepJoint 分割用于根据提取的背景图像分割图像。最后，借助 GAN 成功完成了图像标注。因此，图像标注使用生成的基于 ESFO 的 GAN 的后续结果。使用闪烁数据集，开发的方法显示出增强的结果，最大 F-Measure 为 98.37%，最大精度为 97.02%，最大召回率为 96.64%。图像标注使用产生的基于 ESFO 的 GAN 的后续结果。使用闪烁数据集，开发的方法显示出增强的结果，最大 F-Measure 为 98.37%，最大精度为 97.02%，最大召回率为 96.64%。图像标注使用产生的基于 ESFO 的 GAN 的后续结果。使用闪烁数据集，开发的方法显示出增强的结果，最大 F-Measure 为 98.37%，最大精度为 97.02%，最大召回率为 96.64%。