Image retargeting aims to alter the size of the image with attention to the contents. One of the main obstacles to training deep learning models for image retargeting is the need for a vast labeled dataset. Labeled datasets are unavailable for training deep learning models in the image retargeting tasks. As a result, we present a new supervised approach for training deep learning models. We use the original images as ground truth and create inputs for the model by resizing and cropping the original images. A second challenge is generating different image sizes in inference time. However, regular convolutional neural networks cannot generate images of different sizes than the input image. To address this issue, we introduced a new method for supervised learning. In our approach, a mask is generated to show the desired size and location of the object. Then the mask and the input image are fed to the network. Comparing image retargeting methods and our proposed method demonstrates the model's ability to produce high-quality retargeted images. Afterward, we compute the image quality assessment score for each output image based on different techniques and illustrate the effectiveness of our approach.

翻译：图像重定向旨在根据图像内容调整图像尺寸。训练用于图像重定向的深度学习模型的主要障碍之一是缺乏大规模标注数据集。在图像重定向任务中，标注数据集无法用于训练深度学习模型。因此，我们提出了一种新的有监督方法来训练深度学习模型。我们将原始图像作为真实值，并通过调整大小和裁剪原始图像来创建模型的输入。第二个挑战是在推理时生成不同尺寸的图像。然而，常规卷积神经网络无法生成与输入图像尺寸不同的图像。为了解决这个问题，我们引入了一种新的有监督学习方法。在我们的方法中，生成一个掩码来显示对象的目标尺寸和位置。然后，将掩码和输入图像输入网络。通过比较图像重定向方法和我们提出的方法，展示了模型生成高质量重定向图像的能力。之后，我们基于不同技术计算每张输出图像的图像质量评估分数，并证明了我们方法的有效性。